Research to develop evaluation techniques for operational convective cloud modification

Report SOSMT/IAS/R-85/02
RESEARCH TO DEVELOP EVALUATION TECHNIQUES FOR
OPERATIONAL CONVECTIVE CLOUD MODIFICATION
PROJECTS
By: p. L. Smith, J. R. Hiller, Jr.,
A. A. Ooneaud. J. H. Hirsch,
O. L. Priegn1tz. P. E. Price,
K. J. Tyler. and H. D. Orville
Prepared for:
North Dakota Weather Modification Board
P. O. Box 1833
Bismarck, NO 58502
January 1985
Contract No. WMB-IASw80wl
Institute of Atmospheric Sciences
South Dakota School of Hines and Technology
Rapid City, South Dakota 57701-3995
Report SOSMT /IAS/R-85/02
RESEARCH TO DEVELOP EVALUATION TECHNIQUES FOR
OPERATIONAL CONVECTIVE CLOUD MODIFICATION
PROJECTS
By: P. L. Smith, J. R. Miller, Jr.,
A. A. Doneaud, J. H. Hirsch,
D. L. Priegnitz, p. E. Price,
K. J. Tyler, and H. O. Orville
Prepared for:
North Dakota Weather Modification Board
P. O. Box 1833
Bismarck, NO 58502
January 1985
Contract No. WMB-IAS-80-1
Institute of Atmospheric Sciences
South Dakota School of Mines and Technology
Rapid City, South Dakota 57701-3995
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ABSTRACT
This report summarizes some results of a broad investigation with
the objective of developing lrrproved techniques for evaluating opera­tional
convective cloud seeding projects. The research was based on
data collected in conjunction with the North Dakota Cloud Modification
Project (NDCMP) and emphas i zed the use of weather radar data and
numerical cloud m:Jdels. The research included the following
specific topics:
1) Radar echo climatology, especially as related to
potential cloud seeding opportunities.
2) Assessment of operational effectiveness in the NDCMP.
3) A variety of radar data analyses.
4) Estimation of the potential for dynamic seeding using
numerical cloud models.
5) Simulation of NDCMP seeding operations.
Some of the investigations as, for example, a study of first echo
temperatures also considered possible microphysical effects of the
seeding.
The NDCMP involves seeding for both rain enhancement and hall
suppress1on, but the research emphasized evaluation of the rain
enhancement aspect of the project. No definite evidence of rainfall
increases was obtained, but the results were generally consistent
with the seeding hypotheses and the intended effects of the seeding.
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TABLE OF CONTENTS
~
ABSTRACT •• •••••• •••. ••••••••. •••••••••• ••• ..• ••••• •.•• •••••••••• ii i
LIST OF FIGURES................................................. vi i
LIST OF TABLES •••••••••••••••••••••••••••••••.•••••••••••••••••. xi i i
TABLE OF ACRONYMS AND SyMBOLS ••••••••••••••••••••.••••••••••••••
1. INTRODUCTION ••••••••••••••••••••••••••••••••••••••••••••••••
1.1 Historical Background ••••••••••••••••••••••••••.•••••••
1.2 Scientific Background ••••••••••••••••••••••••••••••••••
2. DATA ACQUISITION AND REDUCTION ••••••• ••• •••• 5
2.1 Radar Data Acquisition Procedures 5
2.2 Radar Data Reduction Procedures •••• •••• ••••• •••. ••••••• 10
2.3 Collection of Rawinsonde Data ••••• ••• ••••• 11
2.4 Seeding Tracks ••••••• ••••••••. ••••• •••• ••• ••••• •••••••• 15
3. RADAR ECHO CLIMATOLOGy.... ••••••••• ••••••••• 16
3.1 First Echo Development ••••••• ••• •••••• ••• 16
3.2 Distr1bution of Maxirom Echo Heights •••••••••• ••• ••• ••• 17
3.3 Distribution of Maximum Equivalent
Radar Reflectivity Factors 19
3.4 Distr1butions of Cluster Durations ••••••••••••••••••••• 20
3.5 Distributions of Area-Time Integrals
and Rain Volumes ••• 22
3.6 Distributions of Rain Amounts by
Reflectivity Factor •••••••••••••••••••••••.••••••••• 23
4. ASSESSMENT OF OPERATIONAL EFFECTIVENESS.. 24
4.1 Distributions of Seeding Events 25
4.2 Effectiveness of Hail Suppression
Seeding Operations •••••• •••••••••. •••• •••••• •••• 25
4.3 Case Studies ,. •••. 29
5. RESULTS OF RADAR DATA ANALYSES 33
5.1 Relationships Between Echo Height
and Rain Volume 33
5.2 Relationships Between the Area-Time
Integral and Rain Volume 36
5.3 Average Rainfall Rates During Storms 40
5.4 Correlations Between Echo Cluster
Characteristics ••.. •••••• ••• ••••••• ••••. ••• •••.•••• 42
5.5 Rainfall Comparisons..... 44
TABLE OF COHTENT5
(cont I nued)
5.6 Possibility of Range Bias in
the Observations •• ••• ••••••••••••••.•..•••. .••••..• 48
5.7 Development of Climatological Z~R
Relationships for Convective
Storms in the Northern
Great Plains ••••••••••••••••• ••••••••.. •••••••••••• 51
6. NUMERICAL CLOUD MODELING STUDIES •••••••••••••••••••••••••••• 58
6.1 Assessment of Dynami c Seedi ng Potenti al
with a One-Dimensional. Steady-State
Cloud Model.... •••••• •••••••.• ••••• ••••.• •••. ••••••• 58
6.1.1 Analysis of growth in cloud top
height (1981 data) ••••• ••••• •••••••• ...... ••• 58
6.1.2 Analysis of increases in updraft
speed (1981 data) ••••••••.•• •••• •••. ••• •••••• 62
6.1.3 Model analysis of 1982 Dickinson
soundings •••••••••••••••••••••••••••••••••••• 64
6.2 Comparison of Model Predicted Cloud
Top Growth Due to Seeding with
Observations •••• ••••• ••••••••••••••••• .••••••••••••• 68
6.2.1 Background .••••.••.••••••••• ••••••. ••••••••••••• 69
6.2.2 Radar data •••••••••••••••••••••••••••••••••••••• 71
6.2.3 Cloud model runs ••••• ••••.•• •••••• •••••• •••••.•• 72
6.2.4 Atte~ts to fit the llDdel to
observed cloud heights 75
6.2.5 Other observations ••••••• ••• •••..• ••••••••••• ••. 79
6.3 Sirlulation of Seeding Effects in a
Two-Dimensional, Time-Dependent.
Numerical Cloud Hodel.. ••••• •••••••. ••••••••••• ••••. 80
6.3.1 Overview of sinulation runs
and co~arison data 81
6.3.2 Sinulation of 22 June 1982 case •• ••••• 82
6.3.3 Attempts to s;rrulate the
8 July 1982 case •••••••• ••••• ••••••. 87
7. CONCLUDING REMARKS.......................................... 88
ACKNOWLEDGMENTS ••••••••••••••••••••••••••••••••••••••••••••••••• 89
REFERENCES ••••• ••••• •••••••• ••••••• ••••• ••••• ••••••• •••• •••••••• 90
LIST OF FIGURES
Map show1 ng the locat ions and 150-km range
coverages of the fIDCMP District I (Bowman)
and District II (Parshall) radars •••••••.•••••••••••••
Schematic of operational and research areas
and equipment allocation for the 1982 field
study ••••••••••••••••••••••••••••••••••••••••••••••.••
Plot of the number of days for which radar
data were recorded at Dickinson by time
of day ••••••••••••••••••••••••••••••••••••••.•••••••••
Schematic diagram of the data reduction
procedure used for the NOCMP weather
radar data tapes ••••••••••• ••••••••. ••••••. ••• ••• ••••. 10
Example of a low-ti It PPI map for the
District I (Bowman) radar at 23:01 GMT
on 15 August 1981 •••• •••••••••••••• •••. •••••• •••••...• 12
Frequency distribution of 155 soundings
taken in 1981 at Baker, MT, vs. time
of day...... ••••• •••. •••••••••• ••••••••• •••• •••••••••. 14
Frequency distribution of 31 soundings
taken at Dickinson, NO, in 1982 vs.
time of day.. •••••••••••••• ••••••••• •••••••• ••••• •••.• 14
Composite frequency distribution of the
times of ori91n of the cluster echoes,
for the 1981 80wman radar data •••.••••••••••••••••.••• 17
Percentage frequency distribution of
333 first-echoes vs. temperature •• ••• ••• ••• •••• ••••..• 18
10 Percentage frequency distribution of
MEH values for 351 Dickinson (1982)
echo clusters •••••••• •••• ••••• •••• ••••• •••. •••• ••• •••• 18
11 Cumulative frequency (%) distribution of
maximum equivalent radar reflectivity
factor values ••• ••••••• •••• ••••• ••••••• •••. ••••• ••• ••. 20
12 Cumulative frequency distributions of
radar echo cluster durations •••••• ••• •••• ••• ••• 21
13 Cumul at i ve frequency di stri but; ons of
1982 Oickinson cluster durations ••••• •••••• ....... •... 21
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LIST OF FIGURES
(cor1tinued)
Number .!.i..!..!.£. ~
14 Relative frequency distributions of the
25-dBz ATI and RERV for echo clusters
observed in July 1981, Bowman, NO •••••..• •••. •••••• ••• 22
15 Cumulative frequency distributions (%)
of rainfall volume vs. equivalent radar
reflectivity factor (Ze) for 1981 Bowman
and 1982 Dickinson data •••• 23
16 Spatial distribution of 1981 seeding
events for NDCMP District I •••••• •••••• ••• ••••• 26
17 Distribution of 19BO NDCMP seeding events
for District I, by time of day........................ 27
18 Distribution of 1980 NDCMP seeding events
for District Il, by time of day....................... 27
19 Histogram showing the distribution of
timeliness of initial seeding events
for hail suppression using a 45-dBz
hai 1 threat threshold '0' ••••• 0" 28
20 Same as fig. 19, but for a 50-dBz
threshold •• ••••••••• •••• •••••• ••••• ••••. .•••••••••• ••• 28
21 Same as Fig. 19, but using 11 45-dBz
reflectivity threshold and a MEH
threshold of 35,000 ft (10.7 km) 30
22 Same as Fig. 19, but usin9 a 50-dBz
reflectivity threshold and a MEH
threshold of 35,000 ft (10.7 km) 30
23 Scatter plot showing the relationship
between maximum radar echo heights and
radar estimated rainfall volumes for
the 1982 clusters ••••• •••••••.•• ••• ••••.•.•.. •••••.••• 34
24 Scatter plot and regression line of ec.ho
cluster rain volumes vs. 25-dBz area-time
integrals for 1981 Bowman data 3B
25 Scatter plot comparing RERV from echo
clusters with thei r ATI •••••• ••••••• 39
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LIST OF FIGURES
(continued)
Humber Title ~
26 Cof'l1)arison of echo cluster ratn volumes
cOlllluted from Z-R conversion and inte­gratton
with corresponding volumes
estimated from the 1980 rain volume
vs. 25-dBz ATI formul a •••••••••••••••••••••••••••••••• 40
27 The relative frequency distributions
of cluster average rainfall rate for
growing and decayin9 periods 42
28 Comparison of frequency distributions
of the RERV for seeded and non~seeded
echo clusters for the 1981 80wman data 47
29 Comparison of frequency distributions of
the RERV for in-district and out-of­district
echo clusters; 1981 District I
(Bowman) data ••••••••••••••• ••••••••• •••• ••••• ••• ••••• 47
30 Comparison of frequency distributtons of
the maximum reflect1vity factors for
nearby and Il'()re distant echo clusters;
1981 80wman data.. •••••• •••••••• •••••••• ••• ••• 49
31 Two-dtmensional frequency distributton of
maxillUm equivalent radar reflectivity
factors for 851 echo clusters as a func­tton
of range from the radar (1981
80wman and 1982 Of cl:. t nson data) 49
32 Comparison of cUlOOlathe frequency
distributions of reflectivity factor
values based on areal coverage for
in-district and out"'Of-dtstrict
clusters; 1981 Bowman data 50
33 Comparison of cumulative frequency
d1 str1 but ions of reflect i vity factor
values based on areal coverage for
In-district and out-of-district
clusters; 1982 Oick-inson data.......... ........ ....... 51
34 Graph showing observed cluster HEH as
a function of range from the radar 52
LIST OF FIGURES
(continued)
35 Cumulative frequency distribution of
5-cm equivalent radar reflectivity
factors> 42 dBz obtained during
1982 in southwestern NO •••••••••••••. ...... •••••• ••••. 53
36 Map showing the locations of the Dickinson
radar site and the Newell Agriculture
Research Station's watershed studies •••••••••••••••••. 54
37 Curnul at i ve frequency di stri bution of 5-mi n
rainfall rates observed over the period
1957-1970. using data from the Newell ARS
site ••••.• 55
38 Frequency distribution of maximum model
predicted cloud top growth expected due
to seeding usin9 1300 MDT (1900 GMT)
1981 Baker sounding data as input •.•. 60
39 Frequency di stri but ion of rrode1 predi cted
lIH values due to seeding for 5-km diameter
updrafts; 1300 MDT Baker 1981 soundings
used as input 60
40 Frequency distribution of cloud model
predicted increases in maximum updraft
speed due to seeding; 1300 MDT Baker
1981 soundings used as input 62
41 Model-predicted seedability vs. observed
seeding effect for 14 seeded and 9 control
clouds stUdied in 1965 .. 70
42 Observed seeding effect vs. predicted
seeding effect for the initial "ll.H"
calculations •••••••• .... ••••••••••• •••••. ••• ••••• ••••• 75
43 As in Fig. 42, except that the rrodel
cloud heights were found by running
the model in an inverse rrode .. 78
44 Observed cell radar echo radius vs. the
inferred roodel cloud updraft radius.. 79
LIST OF FIGURES
(continued)
Number Title ~
45 Plots of the maximum mixing ratio
of graupel in the sill1Jlated clouds
as a function of time 85
46 Plots of the maximum mixing ratio
of snow in the simulated clouds
\IS. time ••••••• •••••••••••••••••••••••• •••••••• •••. ••• 85
47 Comparisons of accumulated rain at
the ground at 75 min roodel time 86
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LIST OF TABLES
Radar System Specifications for
EEC WR-I00 ••••••••••••••••••••••••••••••••••••••••••••
Summary of Radar Data Collected .
Reflect; vity Factor Code Used for
Low-Tilt PPI Printouts •••••••• ••••••••••••••• •••• ••••• 13
COllllari son of Fi rst-Echo Data from 1982
Dickinson. NO, Observations With Those
1n Koscielski and Dennis (1976) •••••••• •••••• ••••••••• 19
Regression Parameters for Cluster Echo
Height - Rain Volume Relationships
(1982 Dickinson Radar Data) ••••• ••••••• •••••. 35
Average Values of Some Echo Cluster
Characteristics ••••••••••••••••••••••• ••••• ••• •••. •••• 43
Correlation Coefficients Between Echo
Cluster Characteristics for the 1982
R.adar Data •••••••• ••••••••••• •••• •••• ••••• •••••••••••• 43
Comparison of Average Values of Some
Characteristics of Seeded and Non-
Seeded Echo Clusters (1982 Data) 45
Comparison of Correlation Coefficients
8etween Echo Cluster Characteristics
for Seeded and Non-Seeded Clusters
(1982 Data) •••••••••••• ••• •••• •••••• ••••••••••• ••••••. 46
10 Rainfall Rates Calculated from Different
Ze-R Relationships ••••••••.•• •••• •••••••••••. ••••• •••• 56
11 Model-Computed llH Values (km); 1300 MDT
(1900 GMT). Baker, MT, 1981 Sounding nata ••••• 59
12 Summary of llH Values; 1981 Baker, MT,
1300 MOT (1900 GMT) Sounding Data ••...• 61
13 Model-Computed bW Values (km); 1300 MDT
(1900 GMT), Baker. MT, 1981 Sounding Data ••••••••••.•• 63
14 Summary of liW Values; 1981 Baker, MT.
1300 MDT (1900 GMT) Sounding Data •• 64
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15
L1ST OF TABLES
Title
!"()del-COll1>uted AH Values - 1982
Dickinson Soundings •.••••.••••••••..••.• 65
16 Sunmary of Maxil11Jm AH Values - 1982
Dickinson Soundings •.••••• •••• ••• ••• ••••••• ••••••.•••• 66
17 Increase 1n Maximum Updraft Speed
(1111) DJe to Seeding 1982 Dickinson
Soundings; S-k.m Updraft Diameter 67
18 Matrix of Symbols for Cloud Top
Height Values ••••••••••••• •••••• ••. •••••• ••• •••••••••• 69
19 Results of l1H Study for Seeded
Cells ••• ••••••• ••••••••••••••••.••.. ••••• •••.••••.••.• 73
20 Results of tl.H Study for Non-Seeded
Cells ••••••••••••••• .••• •••••• •••••.•• ••••••••••••••.• 74
21 Results of Inverse-Hode AH Study
for Seeded Ce 11 s ••••.•••••••••••••.•••••••••••••••...• 76
22 Results of Inverse-Hode AH Study
for Non-Seeded Cells ...••••••••••••••••••••••••••••.•• 77
23 Total Precipitation •••••••••• •••••• ••••••••••••••••••• 86
-xiv-
AT!
BAK
BIS
BOl~ or BOO
°c
CCOPE
COT
OME
CSU
OIK
DVIP
GMT
GGW
GPCM
lAS
LOT
MDT
MEH
MLR
MLS
MST
NCAR
NOeM?
NOPP
TABLE OF ACRONYMS AND SYMBOLS
Area time integral
Baker, MT
Bismarck, North Dakota
Bowman, North Dakota
Degrees Celsius
Cooperative Convective Precipitation Experiment
Ceotral Daylight Time
Distance Measurinlj Equipment
Colorado State University
Dickinson, North Dakota
Digital Video Integrator and Processor
Greenwich Mean Time
Glasgow, Montana
Great Plains Cloud Model
Institute of Atmospheric Sciences
Local Daylight Time
Mountain Daylight Time
Maximum echo he; ght
Multiple linear regression
Miles City, Montana
Mountain Standard Time
Nat i Dna 1 Center for Atmospheri c Research
North Dakota Cloud Modification Project
North Dakota Pi lot Project
TABLE Of ACRONYMS AND SYMBOLS
(continued)
NOWHO North Dakota Weather Modi fication Board
NOAA National Oceanic and Atmospheric Administration
PAR Parsha 11, North Dakota
PPI Plan Position Indicator (radar screen)
PRF Pulse Repetition Frequency
RERV Radar estimated rain volume
SDSM&T South Dakota School of Mines and Technology
UNO Uni versity of North Dakota
VDR VHF Omni Range
WMI Weather Modification. Inc.
ZMX Maximum equivalent radar reflectivity factor (dBz)
toH Hodel predicted cloud height change due to seeding
boW Hodel predicted change in maxill1Jm updraft
velocity (w) due to seeding
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1. INTRODUCTION
This report summari zes research conducted under the referenced
contract from May 1980 through July 1984. The research was carried
out as part of a federal/state cooperative program to develop il1llroved
eval uat i on techni ques for operat iona 1 weather nodi fi cati on projects.
The agency responsible for the federal side of the overall program is
the Nat i ana 1 Oceani c and Atmospheri c Adm; nistrat i on (NOAA). The North
Dakota Weather Modification Board (NDWMB) is the state agency that
coordinates this part of the program. which is directed towards the
eva1uat i on of projects deal i ng with sunmer convect i ye clouds.
The North Dakota Cloud Modifi cat i on Project (NOCMPj. conducted
for both precipitation enhancement and hai 1 suppression by seeding
such clouds with ice nucleating agents. was selected as a "test bed"
for this study. The contract under which this report was prepared
is one of several coordinated by the NOWMB in an effort to develop
improved physical and statistical techniques for evaluating such
projects. Application of the resulting techniques to operational
projects such as the NDCMP should lead to a better understanding of
the effects of ice phase seeding on conyecti ye clouds.
The South Dakota School of Mines and Technology research
concentrated on those aspects of the evaluation problem that can
be examined primarily by using quantitative weather radar data
and numerical cloud fT(ldels. The research under the contract han
several mai n objecti yes:
1) To develop climatological information to ain in
defining the potential for cloud seeding opportunities
in the NDCMP;
2) To investigate the potential for dynamic seeding in
North Dakota through data analysis and the application
of numerical cloud models to the NDCMP region;
3) To assess the effectiveness of the NOCMP operations;
aod
4) To develop evaluation techniques for operational
weather fT(ldification projects based on the use of
di gital weather radar data.
1.1 Historical Background
The evaluation of operational weather modification projects has
yielded a substantial amount of useful scientific information in the
past. Changnon et al. (1979) recently stresseo again the importance
of an evaluationcapability for such projects. Smith et al. (1973)
conducted some eva luat i on studi es of South Dakota 's operatT'Ona 1
-1-
weather nodification project, which was similar to the current
NDCMP, using radar and sounding data. However. IIDst of the research
related to evaluating operational projects has involved statistical
approaches. For example, the Illin01s State Water Survey has been
developing statistical techniques for evaluating operational weather
modification projects (Hsu et al., 1981; Hsu and Changnon. 1984).
Gabriel and Petrondas (1983/emined some of the statistical
procedures cOlTHOOnly used for evaluating operational projects.
The present federa 1Istate cooperat 1',Ie program was developed in
response to recommendations made by the Weather Modification Advisory
Board (l978). The responsibility for carrying out the federal side of
this program to evaluate ongoing weather rrodification projects, both
summer and winter, was assigned to NOAA. Colorado State University
(CSU) developed a general design for the program under a NOAA contract,
and the design of the investigations in North Dakota has been based on
recolllllendations deri ',led from the CSU work. The work in North Dakota
was conducted through a cooperat i ',Ie agreement between NOAA and the
NDWMB.
Field work began in North Dakota in tile sunmer of 1980 with tile
collection of digital weather radar data at two of the NOCHP district
radar sites (Bowman and Parshall). A previous report (Brown et a1.,
1980) describes the collection of the data in 1980 and Smith et'iT":'"
(1981) discuss the pre11minary analysis of those data. For the SUiilmer
of 19B1, the investigation focused on trying to establish the validity
of the dynamic seeding approach in North Dakota. The measurement
capability was expanded to include an aircraft equipped with basic
cloud physics instrumentation; that a1rcraft was operated by the
University of North Dakota (UNO). Miller et a1. (1981) SUlll'Rclrize
the radar data collected from the same sites-a5"""in 1980. Another
report (Smith & lli. 1982) sultll1ari zes the pre1imi nary ana lyses of
the 1981 data and also provides a rore complete description of the
1980 data analysis.
For the sult'lTer of 1982, additional 1~roveraents were made to the
project design and to the facilities. The scientific e~hasis was
broadened to include investigation of microphysical as well as dynamic
effects of seeding. A radar and raw1nsonde system dedicated to tile
research function were operated at Dickinson. The UNO operated a
cloud physics aircraft with improved performance and instrumentation.
Smith et & (1983) discuss the data collection and preliminary
analysTS work for the 1982 season. Since that time the effort has
been devoted to data analysis, and the present report contalns the
main results of all of the analyses carr1ed out under the contract.
Partial reports of some of these results can also he found in Miller
!!..!h (1983). Ooneaud!!.!b. (1984a.b), and Smith!!..!h (1984a,b).
1.2 ScientHic Background
The North Dakota Cloud ModHication Project, like any other weather
modifi cat ion project, involves three primary aspects:
-2-
1) Identification of seeding opportunities.
2) Treatment of the clouds.
3) Evaluation of the results of treatment.
The operational procedures for the NOCMP are spelled out in a
detailed Operations Manual (NDWMB, 1980). The research discussed
herein emphasizes evaluation of the effects of the seeding, although
the identification and treatment aspects of the project were also
explored.
The operational cloud seeding in the NDCMP is conducted using
glaciogenic nuclei in an atterrpt to freeze supercooled cloud liquid
water and/or raindrops. The objectives include both precipitation
enhancement and hail suppression. Regardless of the details of the
mechanism by which precipitation is produced, such ice phase seeding
can, and probably does, influence both the microphysical and dynamic
characteristics of the clouds. The principal microphysical effect is
to accelerate the development of precipitation, or to cause it to occur
at lower altitudes in the seeded clouds. This microphysical change,
however. rust inevftably feed back into the dynamic behavior of the
clouds. That can occur in at least two ways:
1) The freezing of the supercooled liquid releases latent
heat of fusion which, under the appropriate circum­stances,
increases the buoyancy and the associated
updraft speed; and
2) Increased precipitation loading due to the earlier or
lower development of precipitation may modify the up­and
downdrafts; the associated outflow in the houndary
layer may, in some circumstances, lead to secondary
growth of clouds neighboring the ones seeded.
These effects have been explored in a numerical cloud IIDdel by Orville
and Chen (1982).
In some projects, glaciogenic seeding is conducted primarily to
affect the dynamic behavior of the clouds. Elementary indications of
dynami c effects of seedi ng are avai 1ab1e from one-di mens i ana 1, steady­state,
numerical cloud models such as the Great Plains Cloud Model
(Hi rsch, 1971). The effects of seeding on vertical cloud growth as
indicated by such roodels have been confirmed for tropical clouds
(Simpson et a1., 1967), but direct confirmation for clouds of the
northern "'H'igDlains is lacking. Such IIDdels are also not very
satisfactory for estimating the amount of precipitation that results.
Results of the North Dakota Pilot Project (Dennis et al •• 1975a)
showed evidence of rainfall increases on days for Wh";'ctlthis type of
cloud IIDdel indicated appreciable dynamic growth due to the seeding.
Perhaps the nost detailed hypothesis invoking the dynamic seeding
-3-
approach is that described by Simpson (1980). Dennis and Schock
(1971) found evidence of dynamic effects of seeding in experiments
carried out in the Dakotas.
The NDCMP Operations Manual ;s not very specific about the nature
of the dynamic effects expected. In fact, the CSU investi9ators raised
quest Ions about whether the seedi ng rates used are adequate to cause
significant dynamic effects in the clouds that are seeded primarily for
rainfall enhancement. However. they did suggest that in those NDCHP
-Type 8- clouds that are seeded for hal1 suppression. the seeding
rates might be adequate to produce dynamic effects that could enhance
the rainfall. These Type B clouds are rou9hly equivalent to the
"convect; ve compl exes II ; dent; f; ed in Montana stud; es by He; mbach and
Super (1980) or the "small mesoscale areas" of Austin and Houze (1972).
ThUS, one goal of this research was to clarify the applicability of
the dynamic seeding concept in the North Dakota clouds.
-4-
2. DATA ACQUISITION AND REDUCTION
2.1 Radar Data Acquisition Procedures
The radar systems used in support of this investigation were
Enterprise Electronics Corporation WRIOO-2 systems with basic charac­teristics
as summarized in Table 1. These small C-band radars have
been used for directing the cloud seeding operations in the NOCMP. In
fact, for the first two years of field work under this project the data
were actually recorded from operations control radar sets. That pro­duced
some conflicts between the requirements for directing the seeding
operations and collecting data for the research. so for the third field
season, a separate radar was dedicated to the research function.
The radar sites for the first two surrmers (1980 and 1981) were at
Bowman and Parshall (Fig. 1) field headquarters for NDCMP Districts I
and II, respectively. For the third year (1982). a single research
radar was located at Dickinson (Fig. 2), in between the two NDCMP target
districts. The move was intended to improve the data for the target
districts in two ways. One was that the life histories of radar echo
clusters in the districts would no longer be interrupted by the absence
of data from a 20-km radius "donut hole" around the radar site. Echoes
from withi n thi s 20 km ci rcle were not recorded because of ground
clutter contamination and the requirement to go to excessively hi9h
elevation angles. The second improvement came about because the radar
scan cycle could be speeded up as use of the radar no 10nger had to he
shared with the NOCMP fi e1d rreteorol ogi sts.
Generally. data were recorded whenever there was significant
convective activity in the region within, roughly, a 150-km range from
a radar site. This included times when the NDCMP cloud seeding opera­tions
were in progress. as well as frequent occasions when no seeding
was being carried out. The recorded data include flI1Cf1 weather acti­vity
outside the NDCMP districts to provide a sample of unseeded
cases for comparisons with cases in the districts themselves. Data
from several occasions involving stratiform precipitation were also
recorded. but there has not been l1lJch detailed analysis of those data.
The radars were operated in a volume scan mode. with two low level
360° azimuth scans at P elevation angle and succeeding scans at 1°
elevation increments. The elevation steps used were smaller than the
2° elevation beaiTh'lidth of the radar antenna because better resolution
for the determination of echo heights was desired. For 1980 and 1981.
the scans were continued to an elevation of 15°, or until no echoes
were observed on an azimuth scan (if a lower elevation angle took the
antenna beam above the cloud tops). This scan cycle required about
6 min to complete, with the antenna rotating at about 22 sec per revo­lution.
The objective was to complete one scan every 10 min; the
remaining time was used by the NDCMP radar meteorologists to collect
observat ions needed for di recti ng the seedi ng ope rat ions. Thi soften
-5-
TABLE I
Radar SystcllI Specifications for Efe \'tR-IOO
Transmitter
Wavelength, ),
Pulse duration, 'l:
Pulse repetition frequency, F
Peak power output, Pt
Antenna
Refl,ector size
Antenna pattern
Antenna gain
AziJm.lth readout
Elevation
Elevation readout
Polarization
Receiver
folini'lium detectabl e
signal. Smin
Operating frequency, f t
Response characteristic
5.4 cm
2J,Jsec
256 s-l
200 kW nominal
1.S3 m diameter
2.1 0 conical beam
37 dB nominal
Digital to 10.10
-2 to "'600
Digital to 10.1 0
Horizontal
From calibration
5600 MHz
Logarithmic
!signal Processing
Digital Video Integrator and Processor (DYIP)
-6-
proved insufficient when many echoes were in the area so. in practice,
the scan interval sometimes increased to 12 min or roore. This con­flict
between requirements for using the radar to acquire research
data and observations for conducting seeding operations led to the
decision 1n 1982 to provide a separate radar at Oickinson for the
research program. The max11JlJ1Il elevation angle was then reduced to
12°, and the scans were routinely conducted on a 6-min cycle.
The radar systems were calibrated according to customary
procedures. The data system and the calibration procedures were
upgraded each year on the basis of previous field experience, so that
continual refinements took place. A cOfllllete receiver/transmitter
calibration was usually carrfed out twice per week to obtain values
for the transmitted frequency, power, and pulse repetition frequency.
The pulse shape and duration were checked occasionally, and the
antenna gain was measured once each season. The antenna orientation
was first Checked by solar rethods in 1982. so there may be some
errors in the elevation angle data from the previous seasons.
Receiver calibration data were usually recorded on the tapes before
and also after each data recording session. This led to values for
slope and intercept parameters for a linear fit to the receiver
calibration curve for each set of recorded data.
-7-
Montanill
Wyoming
North Dakota
f:i..!L..~: Schematic of operational and research areas and equipment
iTTOcation for the 1982 field study.
The data recording format and the data handl i ng procedures were
simi Jar to those used in HIPlEX (see Schroeder and Klazura. 1978. for
details). Data were recorded at 1° azimuth 1ncrements with l-km range
bins along the radar beam. The video integration employecl 16 pulses at
a radar PRF of about 256 pulses per second. During each scan, an azi­IIIlth
sector of about 20° or larger was omitted froo the recorded data
to permit the antenna to step up to the next elevation angle whi Ie
passing through this Mblank sector." The sector location was adjusted
by data collection personnel to minimize loss of significant data.
The first 20 km of range clata were electr0f'l1cally discarded to elimi­nate
the majority of the ground clutter from the recorded data. but
considerable clutter still affected some of the data at the lower
elevation angles.
Table 2 sUmll\ar1zes the radar data recorded during this project at
the different sites. The variations are due to a combination of flEteo­rolog1cal
factors (e.g •• 1980 was very dry in western North Dakota),
varying length of the research field seasons (the 1982 season was only
one mnth in duration). equipment difficulties (especially 1n early
1980). dnd other factors. Figure 3 shows thl~ variation of the
-8-
TABLE 2
Summary of Radar Data Collected
Data Collection No. Days No. Hrs. No. of
Year Radar Site Period With Data of Data Data Tapes
1980 Bowman 17 Jul-31 Aug 19 72 25
1980 Parshall 28 Jul-28 Aug 18 51 18
1981 Bowman 1 Jun-23 Aug 51 185 96
19B1 Parshall 1 Jun~20 Aug 46 168 78
19B2 D1ckinson 6 Jun- 9Jul 21 87 43
15r----T-'=-==;r.::.:..:....=="T------,
~
J:
f-
~ 10
(f)
>­<
1. o
u.
o 5
a:
w
<D
:2
:J
Z
~ 1: Plot of the number of days for which radar data were
recorded at Dickinson, by time of day.
-9-
recording activity in 1982 with time of day, indicating the expected
peak of activity in mid-afternoon (1600 lDT or 2200 GNT).
2.2 Radar Data Reduction Procedures
The data reduction procedures used to convert the recorded data
to values of the equivalent radar reflectivity factor were similar to
those described by Schroeder and Klazura (1978). Figure 4 illustrates
the data reduction sequence used. In brief. the raw data tapes
recorded 1n the field were sent to the NDWMB offices in Bismarck for
quick quality control checks and the extraction of calibration
information. From there, the tapes went to the University of North
Dakota (UNO) where they were edited and reformatted into the so-called
"A-fi les." Copi es of the A-fi 1e tapes were then sent to the South
Dakota School of Mines and Technology where they were merged with the
calibration data extracted by the NOWMB and weekly calibration logs
from the field to create radar reflectivity factor files ("dBz
flles"). The latter files contain values of the equivalent radar
reflectivity factor Ze for each aZ1nlJth-elevation-range bin in the
data grid. From them, a variety of printout products were prepared,
of which the low ti It PPJ maps were the most widely used.
~4: Schematic dia(jralllQf the data reductinn rrncerlure u<;,·rj fQr
the N!fCMI' weather radar ddtil t.<lpes.
-10-
The PPI printouts map the Ze values observed at low elevation
angles for sUbsequent analysis. Figure 5 shows an example. Because
of excessive ground clutter. the data from the 30 elevation angle were
used inside 50 km range and the data from 10 elevation outside the
50-km circle. The map scale is such that each value plotted repre­sents
an area of about 3.3 x 4.2 km (13.9 km2). The number of
azimuth-range data h1ns represented by one "point" on the map varies
with distance and direction from the radar, from a minimum of about
6 to a maximum of about gO. The maximum Ze value anywhere 1n the
13.9 km2 area is shown 1n an alphanumeric code indicating 5 dB
increments; Table 3 summarizes the code employed.
The next step in reducing the radar data involved considerahle
time in manually identifying individual "echo clusters" by drawing a
"box" around each cluster for each scan on these printouts. The
intent was to identify as a separate cluster each entity that could be
distinguished from neighboring echoes and followed for a period of
time, and that was likely to be recognized and treated by the NDCMP
operational crews as a unit. The echo clusters are roughly equivalent
to the convective complexes defined by Heimbach and Super (1980), or
the small mesoscale areas of Austin and Houze (1972), although
specffic numerical criteria were not used in this stUdy.
In this process of cluster identification, the occurrence of echo
mergers was handled as follows. If separate echo entities (either
single cells or groups of cells) could be followed for several scans,
they were identified as separate clusters. If two such entities sub­sequently
merged, a new cluster identity was assigned to the rrerged
entity. The cluster life histories contain records of such rrergers,
but for many analyses this situation is represented by three distinct
clusters in the data set.
With the aid of a transparent overlay, azimuth and range
boundaries were then determined for each echo cluster. Each cluster
was also classified as either seeded or un seeded and as to whether it
was inside or outside a target district. To make the seed/no-seed
class ifi cati on, ai rcraft f11 ght tracks were manually plotted on the
radar data printouts with the locations of seeding events indicated
(see Sec. 2.4). Figure 5 illustrates how the clusters were boxed on
the printout and flagged for seeding status. The information from
these "boxes" was then coded and entered into a computer file for each
cluster. Using those data and the corresponding dBz files as input
into a "cluster characteristics program," the values of pertinent
characteri st ics such as echo areas, maxillllm reflect i vi ty factors,
maximum echo heights, and rain volumes were determined.
2.3 Collection.2f. Rawinsonde Data
Upper-air sounding data were collected at Baker, Montana (located
about 70 km northwest of Bowman, North Dakota) as part of the 1981
Cooperative Convective Precipitation Experiment (CCOPE). Due to the
-11-
~ :
.................
-12-
TABLE 3
Reflectivity Factor Code Used for Lo'lf-Tilt PPI Printout.s
Re£lect~~~~f Range
Blank No Echo
10-14,9
15-19.9
20 _ 24.9
25 - 29,9
~O - 34.9
3S - 39.9
40 - 44.9
4S - 49.9
50 - 54.9
55 - 59.9
60 - 64.9
65 - 69.9
proximity to Bowman. those rawinsonde data are also usable for the
NOCMP. During the sUlllTlE!r of 1981, 155 soundings were taken on
49 days. Figure 6 shows a frequency distr1but10n of these soundings
taken on an irregular schedule based on expected convective activity
during CCOP£ versus time of day. Note that the time scale here is in
local (MOT) time. For some unknown reason. the Baker soundings were
archived in MDT instead of GMT. a fact which has caused a good deal
of confusion.
Rawinsonde data for the 1982 field program were taken at Dickinson,
North Dakota. A tota 1 of 31 soundi ngs were recorded on 20 days.
Figure 7 shows a frequency distribution of the number of soundings
versus time of day. Notice that the soundings plotted at 2400
actually correspond to 00 GMT data. Also. thls plot differs from the
one in Fig. 6 in that the time scale is plotted in GMT and not HOT.
-13-
401,-------===------------
JO
1981 BAKER HT
lIME !MOT]
~~: Frequency distribution of 155 soundings taken in 1981 at
Baker. MT, vs. time of day. Note that the time is r~ountain
Day1 i ght Ii me (MDT).
201,-----------
1982 OICKINSON NO
mtE (GMT)
~ik 19~;e~~~n~{m~;~~r~~~~iO~o~: ~~a~O~~~i~{~e t~~e~n a~M~i~~~n~~~t
the vertical scale di ffers in magnitude from Fig. 6.
-14-
All the sounding data were received in magnetic tape form and were
further processed locally to add height information to the significant
levels and to put the data into a format suitahle for il1put to
numerical cloud IT()dels.
2.4 Seeding Tracks
To determil1e which clusters were seeded, flight tracks and seeding
information from the NDCMP seeding aircraft were used. These data are
archived by the NDWMB in Bismarck following each field seasol1.
Copies of these data were obtained on magnetic tape. Each record, or
card image, covers one seeding event from a single aircraft. The data
on each record comprise: VOR-DME position of the ai rcraft, VOR-DME
reference stat ion. fl i ght alt itude. temperature at fl i ght level
(when available). cloud base height (when available), inflow (updraft)
velocity (when available), number of seeding generators used, and the
amount of AgI burned by flares.
To make these data usable with respect to the radar data analyses,
each VDR-DME position was converted to a aZilllJth/range position from
the radar(s). These positions were then manually plotted on the
corresponding low-tilt PPI printouts. Because of the large amount of
effort involved in manually plotting the aircraft positions onto the
PPI's. only those recorded events where seeding actually occurred were
plotted.
It should be noted here that care 1llJst be exercised when
interpreting the aircraft event times. In District I, seeding events
are recorded in Mountain Daylight Time (MDT), while the District II
events are recorded in Central Daylight Time (COT). The radar data
times are in GMT.
-15-
3. RADAR ECHO CLlJIlATOLOGY
One objective of the research was to develop climatological
information for the NOCMP region regarding potential cloud seeding
opportunit ies and the app 1i cabil ity of the dynami c seeding approach.
The general intention was to provide information on the frequency,
locations, and characteristics of seeding opportunities. This infor­mation
was to be generated mainly from the rawinsonde and radar echo
data. Here general information characterizing the radar echo popula­tion
in the NDCMP region was also of interest. This section of the
report discusses the radar echo climatology information obtained.
The summer of 1980 was very dry in the NOCMP region, and usable
radar data were obtained only for about 40 days. Consequently, only
163 echo clusters were identified in the 1980 data from both radar
sites, of which only 8 clusters were seeded. For the wetter summer
of 1981, 583 echo clusters were identified from the Bowman radar data
alone; 75 of them were seeded. Because of some calibration diffi­culties
and other problems, few of the 1981 Parshall radar data are
included in these analyses. The field project in 1982 involved a
season of only fhe weeks duration, with data recorded at a single
radar site (Dickinson), but, nevertheless, 351 echo clusters were
identified. BecaUSe of seeding restrictions i~osed due to the wet
conditions in the region, only 28 of those clusters were seeded.
3.1 ~ Echo Deve1opment
The radar printouts were exallined to identify the !,imes and
locations of the initial radar echoes from the clusters. No pre­ferred
region for echo generation was evident. The distribution
of initial echo tirres over the day (Fig. 8) indicates a peak in
convective activity in the late afternoon (2100-0000 GMT), as
expected. There is some 1ndication of a resurgence of activity
just after sunset (0300-0600 llH), JOOst of which occurred in
August, and a IlininlJm occurs 1n the JOOrnlng (1200-1500 GH).
These features are consistent with the known climatology of
this region.
To gain information about microphysical mechanisms in the North
Dakota clouds, some distributions of first-echo heights and tem­peratures
were determined. The approach followed is similar to that
used by Koscielski and Dennis (1976), and the results are also quite
similar. Isolated first echoes and ones appearing adjacent to
existing echo clusters were identified manually in the data from
12 days in June-July of 1982. The first~echo hei9hts were deter­mined
from the radar elevation and range data, and the corresponding
te~eratures were taken from the nearest appropriate soundin9. Fol­lowing
the usual practice in such studies, no correction was made
for possible higher telllleratures in the cloud interiors.
-16-
-
NUMBER OF CLUSTERS: 583
,----
-
~,---- :,,-1 r
" . ~
IEGINNINGTIMEOFCLUSTERECHO(G.M.T,)
~~: COlJllosite frequency distribution of the times of origin
Ofthe cluster echoes, for the 1981 Bowman radar data.
Figure 9 shows a frequency distribution of the 333 first-echo
tel11>eratures found using 1982 Dickinson data. The median first-echo
te""erature was _8.2°C.
Table 4 displays the main first-echo results and COl1l'ares these
obser....ations wah those reported by Koscielski and Dennis (1976).
Because fewer than 5~ of the 1982 cases were seeded clouds, the values
shown from Koscielski and Dennis are for no-seed days. The means and
standard deviations are quite similar, and the ranges are also com­parable.
The 1982 mean (1 rst-echo tel1l'erature of _9°C suggests that
the main predpitation-forming Ilechanism is probably an ice process.
However. a sizable fraction of cases (more than one-third in 1982)
have first-echo temperatures higher than -SoC. As noted by
Koscielski and Dennis. this seems to suggest the likely ilJllortance
of coalescence processes in at least some of the clouds. However.
particle recirculation processes may also contribute to the
occurrence of "high-temperature" first echoes.
3.2 Distribution Q!. MaxillJJm ~ Heights
A maxillJJm echo height (MEH) was determined for each cluster on
each radar scan. Then for the total cluster lifetime. the overall
lIIaxilll.lm was determined. Figure 10 shows the frequency distribution
of those cluster MEH values from the 1982 Dickinson radar data. They
range from about 2 to 20 km. with a median value of about 8 km. This
is rather simi lar to the distribution reported hy Smith et a1. (1973)
for maxilllJm echo heights in South Dakota, although the latter-was
compiled on a scan-by-scan rdther than cluster-by-cluster basi'S.
-11-
301,---------------------,
~ -40 -~ -~ -~ -20 -15 -10 ~
TEIlPERATURE OF FIRST ECHOES Ie}
10 15
~~: Percentage frequency distribution of 333 first-echoes vs.
temperature. The average fi rst echo temperature (FEl) was _9.3°C
with a standard deviation of 9.4Co.
18 20
~.!.Q.: Percentage frequency distribution of maximum echo height
(MEH) values for 351 Dickinson (1982) echo clusters. The average
MEH was 8.6 km.
~18-
TABLE 4
Comparison of First-Echo Data from 1982 Dickinson,
North Dakota Observations With Those in
Koscielski and Dennis (1976)
Sample size
Fi rst-Echo Height:
Mean (MSL)
Standard Deviation
Fi rst-Echo Temperature:
Mean
Standard Deviation
Extremes
Percent> _5°C
Percent> DOC
333
5.0 km
1.5 km
_9.3°C
9.4Co
+12, _43°C
38
10
Koscielski
and Dennis
~-seedDaYs)
133
_1O.8°C
9.4Co
+5, -29°C
25
7.5
The distribution in Fig. 10 differs noticeably from one presented
earlier (Smith et al •• 1983) for the 1981 Bowman radar data. There
may be two explana'ffOns for the difference: First, the weather during
the 1982 data collection period included a number of relatively cold
stratiform rainfall situations which might shift the distribution
towards lower maximum echo heights. Second, the radar antenna eleva­tion
angles were checked by solar methods in 1982. whereas that was
not done in 1981. Consequently. there is reason to suspect possible
elevation angle errors in the 1981 data. The error in 1981 is esti­mated
to be of the order of 0.5° (recorded va lues too hi gh) based on
the need to correct the antenna alignment by this amount as a result
of the initial 1982 solar measurements.
3.3 Distribution.Q.!. Maximum Equivalent Radar Reflectivity Factors
A similar analysis was made of the cluster maximum reflectivity
factors. The cUlTkllative frequency distribution shown in Fig. 11 was
constructed by combining maximum equivalent radar reflectivity factor
values for 566 echo clusters observed from Bowman (1981) and 302
observed from Dickinson (l982). The values range from less than 2':)
to Ill)re than 65 dBz. and the median cluster maximum reflectivity
factor is about 43 dBz.
-19-
100
80
70
10
oL~_......,=~_,-~_--,--_~.-J_~_--'--_~.-J
15 3 ~ 45 55 65 75
M~XlMUM EiWIV",LENT RAOt.R REFLECTIVITY FA.elDA (dSz)
.E.!..5c..l!.: Cumulative frequency (%) distribution of maximum equivalent
radar reflectivity factor values; 556 echo clusters from Bowman (1981)
and 302 from Dickinson (1982) were combined to generate this graph.
The diagram reveals that 27% of all the recorded clusters exceeded
50 dBz sometime during their life histories. This suggests that about
one storm out of four in North Oaktoa reaches hail threat status
(assuming that 50 dBz is a reasonable 5-cm radar threshold value for
a hail threat). Strong hail potential (Ie). 55 dBz) is indicated in
about 12% of the echo clusters observed. while only 3% exceed 60 dBz.
This supports the idea that the rare events account for roost of the
devastatin9 hail losses.
3.4 Distributions Q.f.. Cluster Durations
Frequency distributions of the echo cluster durations were prepared
separately for each year. For both the 1980 and 1981 data, the median
duration was a little over one hour (Fig. 12). The longest cluster
duration in 1980 was a little over four hours, while the maximum dura­tions
for the wet 1981 summer were ~;omewhat longer. The distributions
are approximately log normal, except for a deficiency of long-lived
clusters. That is probably due to echoes moving out of radar range
before their full lifetimes could be determined. The way echo mergers
were handled in the data reduction (cL Sec. 2.2) may affect the
detailed form of these distributions somewhat.
The distributions for 1982 were similar to those of earlier years
(Fig. 13). except that more short-lived clusters were identified
because of the shorter radar scan cycle. That reduced the median
duration to about 0.8 hour in the 1982 data.
-20-
::,---------------------."
6.0
5.0 • JULY·AUGUST 1980 DATA
F 4.0 0 JUNE·AUGUST 198t DATA
"-
z 3.0
0i 2.0
~ 1.0
O.51.~2:_75-t1O~:;;;20:--7.50:--'.;-0 --;:,,;-;;,;:-,--c,io,----."".•",:O-':,.,
CUMULATIVE FREQUENCY (%)
~ 12: Cumulative frequency distributions of radar echo cluster
duratTOns; 163 Bowman and Parshall (l980) clusters and 587 Bowman
(1981) clusters were used to generate the respective lines.
DICKINSON 1982
x All clusters
Fullilletlmes
Q2 1 ~ W M
CUMULATIVE FREQUENCY (%)
~.!l: Cumulative frequency distributions of 1982 Dickinson cluster
durations. The "x" 's denote all clusters (351) and the dots denote
only that subset of clusters (122) whose full lifetimes were observed
by the radar.
-21-
Questions arose about the possible effects on these distributions
of echo duration due to clusters rooving out of radar range and there­fore
being assigned too short durations. As noted above. that may be
responsible for the deficiency of long-lived clusters indicated in
Fig. 12. A separate distribution was therefore prepared from the 1982
data including only clusters that passed through their complete life­times
within radar range. Figure 13 also shows that distribution;
the overall effect is to shift the whole distribution toward shorter
durations, because this restriction removes many long-lived clusters
from the sample entirely. However. the general indication of typical
cluster lifetimes is not greatly different. Consequently. the evi­dence
about this effect on the distribution remains somewhat mixed.
3.5 Distributions of Area-Time Inteqrals and Rain Volumes
Doneaud et a1. (1984b) show that the Area-Time Integral (ATI) is
closely connecte<lwith the total rain volume for an echo cluster.
Section 5.2 discusses this relationship in roore detai 1. Both of
these quantities have been found to follow approximately log normal
distributions (e.g., Fig. 14). The median All value for the 1981
data sample in that figure is about 80 km2 hr, while the Il'edian
rain volume is about 400 km2 mm. The ITEdian rain volume for the
echo clusters observed in 1980 was only 250 km2 11111, which probably
reflects the generally drier conditions present in the summer of
1980.
3o,------------------------,
NUMBER OF CLUSTE RS " 391
AT'
~-~---J r--.... L--~RERV
I I ___1: '~--: , ,
I L __-.
I I
r--J t_-1 0IO"L,;--'------'10""'-~---.l1072~--1.l,0';-~-IO"'.;=~---"IOL,5--~-,O'
25-dBz AREA4TlME INTEGRAL {km2 hrl
OR RADAR ESTIMATED RA!N VOLUME (km2 mm)
rwr
~.!±: Relative frequency distribut10ns of the 25-dBz area-time
lntegral (ATI) and radar estimated rain volume (RERV) for echo
clusters observed in ,July 1981, Bowman, North Dakota.
-22-
3.6 Distributions of Rain Amounts h Reflectivity Factor
The distributions of rain amounts with rain intensity are of
interest for a variety of purposes. including such matters as the
effects of rain erosion. Such distributions can be conveniently
illustrated in terms of the distribut10ns of rain amounts with
equivalent radar reflectivity factors (Fig. 15). The figure shows
distributions of the rain volumes from all the echo clusters against
the observed equi val ent radar refl ect i vity factors. The "medi an"
value is about 37 dBz. i.e., about half of the rainfall is associated
with le < 37 dBz and half with le > 37 dBz. Using le-R relationships
applicable in North Dakota, this "median le" can be translated to a
"median rainfall rate" of about 6 IlI11 hr- 1• It is interesting to note
that little of the total rain {< 10%.} falls at reflectivity factors
> 50 dBz. This is the part of the storms, however, tnat produces
most of the damaging hail.
The differences between the 1981 and 1982 data in Fig. 15 are
small, indicating consistent performance of the radar system. The
19B2 field season was briefer and included fI'Ore early~summer strati­form
ra; nfa11, so the sense of the di fference in the fi gure is
plausible.
w~
!il 80
z
~
~
0.w, 60
~
ffi
" 40
~
w
>
~ 20
~"
0 60 70 10
Ze' dBz
~~: Currulative frequency distributions (%) of rainfall volume
vs. equivalent radar reflectivity factor (Ze) for 1981 Bowman and
1982 Dickinson data.
-23-
4. ASSESSMENT OF OPERATlOHAl EFFECT! VENESS
Another objective of this research was to assess the effectiveness
of the NDCMP operations and make recommendations for improvement. This
part of the investigation sought answers to questions such as:
1) How well did the NOCMP operators succeed in recognizing
all of the seeding opportunities?
2) Were they able to treat all the opportunities?
3) Was the treatment conducted in the right place and at
the right time?
Questions concerning the types, amounts, I1'Odes of delivery, Mel
effectiveness of the seeding agents employed were not considered
in this study.
This operational assessment was to be carried out after the fact,
and largely with reference to the recorded radar data, a circumstance
which would provide the advantages of hindsight and ample time for
evaluation of the data. However, a serious limitation to the use of
the radar data for identifying seeding opportunities arises because
there is no di rect correspondence between radar echoes and seedi ng
opportunities for precipitation enhancement. Kat every seeding oppor­tunity
is associated with a radar echO; some clouds that never produce
echoes should be seeded to stilftllate the development of precipitation.
At the same time, not every echo represents a seeding opportunity;
some echoes correspond to clouds that are not suitable for seeding for
a variety of reasons. The precipitation may be well developed before
the clouds come within working range of the target districts; the
observed inflows to the clouds may be weak; or the clouds lJIay be
expected to develop so efficiently through natural processes tl'1at the
NOCMP operators elect not to seed them. Sometimes new clouds that
develop in the vicinity of neighboring echoes are not regarded as
suitable candidates for seeding because of expected dynamic or
carryover interactions.
Moreover, radar data were generally collected in this project only
when seeding operations were in progress or regarded as imminent,
because the radar was not manned on a continuous basis. Opportunities
may have occurred outside the periods when the radar data were being
recorded; if so, they would not be reflected in the data set. Thus
the radar data alone are not sufficient to identify the seeding oppor­tunities
for rain stilTRJlation. Consequently. this approach to the
assessment of operationa 1 effect i veness can produce only liPli ted
results regarding the rain enhancement aspect of the NOCMP and was
therefore not carried very far. The situation concerning the iden­tification
of opportunities for hail suppression is somewhat better,
and Section 4.2 discusses some investigations of that aspect of the
NOCMP.
4.1 Distributions of Seeding Events
Information about seeding opportunities may be difficult to
extract from the radar data, but information about seeding events is
obtai nabl e from the records of the NOCMP seedi ng operati ons:-Th'e
seeding events provide some indication of the frequency and location
(in space and time) of seeding opportunities. although for various
reasons there tend to be many more opportunities than events.
The spatial distributions of the reported seeding events for 1980
and 1981 were examined (Smith et al •• 1981, 1982). In the first year,
there seemed to be relatively llttTe seeding activity toward the
upwind (westerly) edges of the districts and perhaps too flJJch activity
near or beyond the downwind (easterly) edges. That may have been
related to the very dry conditions encountered in 1980, particularly
in eastern Montana and western North Dakota. Nevertheless. the
information was passed on to the NOCHP operators. Interestingly, in
1981 more seeding activity occurred along and beyond the upwind edges
of the districts (see, for example, Fig. 16).
The distributions of seeding events in 1980 by time of day were
also examined (Figs. 17, 18). They generally resemble the distribu­tion
of the beginning times of echo clusters presented in Fig. 8.
The resurgence in act i vi ty around sunset is especi a11y evi dent in
Figs. 17 and 18 (note that these figures are plotted in local time).
In comparing these figures, one should note that the numbers of
seeding events will not correspond in any simple way to the number
of radar echo clusters, because 11 cluster seeded for hail suppression
may be associated with several events.
4.2 Effectiveness of .!:!!!.l Suppression Seeding Operations
Data from 28 days using 1981 Bowman radar observations and 11 days
using 1982 Dickinson radar observations were combined to study the
NDCMP hail suppression seeding response times. First, 180 echo
clusters with maximum equivalent radar reflectivity factors). 45 d8z
were identified in the NOCMP target areas. This vallIe is somewhat
above the NOCHP Operations Manual hail suppression seeding threshold
of 40 dBz and should indicate those clusters posing a realistic hai 1
threat. Seventy-four, or 4U, of these clusters were treated during
their life histories. Figure 19 is a histogram showing, for those
treated clusters, the distribution of the time seeding began with
respect to the time of first appearance of a 45-dBz radar echo. Half
of the 74 were treated pri or to, or withi n 7 mi n after, rea chi ng
45 dBz. Thus, only about 20% of the 180 clusters were seeded within a
reasonable time with respect to reaching the 45-dBz threshold.
Figure 20 shows the same kind of diagram using ,1 50-dBz threshnlli
to identify clusters within a district that pose a hili I threat and
should be subject to seeding operations. Of the 128 clusters that
exceeded 50 dBz, 63 (or 49%) were treated. Of those 63, about 591.
-25-
-26-
BOWMAN
SEEDING
150 EVENTS
100
I I III III
-
50 L,
§ 8 ~ ~ 0 § 0 0 0 0 0 0 § ~ 0 g S 0 ~ 0 0 ;i; " " HOUR OF DAY (MDT)
~lL: Distribution of 1980 HOGMP seeding events for District I.
by time of day. An event can be either a flare or a 3-min acetone
generator burn.
200'rTTT.."rrrTTT.."rrrTTl-.:;:rrrTTl
PARSHALL
SEEDING
EVENTS
ISO
~
w 100
o
~
HOUR OF DAY (COT)
~.!!: Distribution of 1980 HDCHP seeding events for District II.
by time of day.
-27-
NO. SEEDING EVENTS
40.,"------'-----'·'--'7--"2"---31~4'--'-'2'--"-5-"'"1
N::::74
30
::l
~
.".. 20 o
.E!i:..!!: Histogram show1ng the distribution of timeliness of initial
seeding events for ha1l suppression using a 45-dBz hail threat thres­hold.
Those cases to the left of zero (min) represent clusters
treated before reaching 45 dBz and those to the ri ght. after.
NO. SEEDING EVENTS
40 0 6 10 2{l 12 7
N::::63
30
*~ 20 ... o
;f. (early) (late)
10
-60 -30 0 30 60
TIME SEEDING BEGAN- TIME Ze~ 50 dBz (min)
~~: Same as Fig. 19. but for i\ 50 dBz tt"lreshold.
-28-
were seeded prior to, or within 7 min of, reaching 50 dBz. Thus
about 30% of the eligible cluster echoes reaching 50 dBz were treated
within a reasonable time.
Close examination of the data revealed that, in most situations,
seeding operations were being carried out on the lI"Ost threatening
clusters. For example, if three clusters rret the NDCMP seeding cri­teri
a at one ti rre, the ai rcraft operat ions were usua 11y concentrat i n9
on the strongest, largest cluster, or two; the weaker one(s} were
neglected by choice. That accounts, in part, for the relatively large
proportion of "hail threat" clusters that were never treated. The
analysis clearly suggests that the limited number of ava11ahle air­craft
prevented the operations personnel from responding to every
threatening storm. Therefore. selections were made and the priority
given to seeding the greatest hail threats. Many echoes meeting hail
threat criteria were consequently left untreated, and many more rain
increase opportunities were ignored in order to carry out the first
NDCMP seeding priority -- treating the roost serious hail threats.
The NDCMP Operations Manual calls for both radar echo intensity
and height criteria to be considered in identifying storms for hail
suppression seeding. Therefore both maxilTMJm echo height data and
reflectivity data were next considered to identify the hail threats.
Figure 21 shows a distribution of the seeding response times for echo
clusters that equaled or exceeded dual thresholds, with equivalent
radar reflectivity factors of 45 dBz or greater, and echo tops at or
ahove 35,000 ft (~10.7 km). Of the 110 such clusters that were iden­tified
from the data for the 11 days in 1982 and only 20 days in 1981,
51 (or 46%) were treated. Of those 51, 69% were treated "on time;"
that is, prior to, or within 7 min of. reaching these combined thres­holds.
Thus, about a thi rd of this total sample of "hail threat"
clusters were treated on a timely basis. This selection, according to
criteria closer to those used operationally in the NDCMP, reflects
somewhat more fa vorablyon the res pons i veness of the hai 1 suppress ion
seeding.
If the criteri a are ra i sed to Ze .. 50 dBz and MEH .. 35,000 ft, the
seeding response remains about the same. Of the 96 such clusters
identified, 45 (or 47%) were treated; of those treated, 71% were
treated "on-time" (Fig. 22).
4,3 ~Studies
The frequent close proximity of seeding aircraft, clouds with
radar echoes, and clouds without echo makes a similar assessment of
the timeliness of rain increase seeding responses in the NDCMP almost
ilJ1lossible, Subjective assessment by personnel observing the seeding
operations from the radar suggests that. in roost cases, some radar
echoes were observed prior to aircraft launch. However, IOOSt clouds
that produce early radar echoes are relatively inefficient and short­lived.
Other clouds usually develop in the same area later, and the
first ones can properly he considered a kick-off signal for seeding
operations.
-29-
NO. SEEDED CLUSTERS
40.,...::'-..:....----"-.::.·-',,·'-"-·-"-,----''-----'--,
N=51
X=-5min.
30
'w"
'<"i 2 ... o
(early)
.j
(late)
'0
°_60 -30 ° 30
TIME SEEDING BEGAN MINUS
TIME Ze ~ 45 dBz & MEH ~ 35,000 ft. (min)
~ll..: Same as Fig. 19, but using a 45-dBz reflectivity threshold
and a maximum echo height threshold of 35,000 ft (10.7 km).
NO. OF SEEDED CLUSTERS
40 4 1 7 7 13 6 4 2 1
N =45
30
'w"
~ 20 o... o
10
oLL..1.....I....LLl--LJ-L.L.l....LLL..L...l...LJ
-60 -30 0 30 60
TIME SEEDING BEGAN MINUS
TIME Ze ~ 50 dBz & MEH;;;. 35,000 It. (min)
~~: Same as Fig. 19, but using a 50-dBz reflectivity threshold
and a maximum echo height threshold of 35,000 ft (l0.1 km).
-30-
Questions concerning the position and ttmeliness of the seeding
treatments can be explored further using a case study approach. For
echo clusters that were treated, one can examine the locations and
times of the seeding with respect to the overall history of the radar
echo development. This turns out to be a somewhat involved process.
as such matters as proximity to the NDCHP district boundaries, time
of recognition of the opportunity versus deployment of the seeding
aircraft, and the like should be taken into consideration.
Some example case studies are presented in Smith et al. (1981,
1982). In the interest of brevity. the detal1s will notl>e'" repro­duced
here. However. some general conclusions that were reacl1ed
on the basis of those case studies can be presented:
a) The NDCMP operators often had seeding aircraft working
in the general vicinity of the ecl10 clusters that were
observed. Seeding was conducted in the vicinity of the
echoes, and at about the proper time. The pos1tioning
accuracy in both the ai rcraft and radar data was not
sufficient to determine. in fine detail. the accuracy
of the seeding locations (e.g., with respect to inflow
areas or other features that mi ght be apparent in the
radar echo conf1gurations).
b) When seeding was intended for rain enhancement, it often
began rather late. generally after a radar echo had
already developec1. Much. if not roost. rain enhancement
seeding should be directed at cloudS that have not yet
produced an echo. "lost of the seeding in the time frame
for which these case studies were carrfed out was
restricted to hail suppression. so that could account
for the seemingly rather late initiation of the seeding
treatment in many of the cases. However, a general
iJ11)ression of late initiation of seeding for rain
enhancement persists. Certainly practical problems
such as nnving the aircraft into position, locating
inflow areas. and the liKe affect the timeliness of
the seeding, but they are difficult to evaluate in
a .E.21!. hoc analysis.
c) A seedi ng treatment was not often followed by immedi ate
signs of growth in echo area, height, or intensity. In
fact. a decrease in one or rore of these indicators was
not unconmon. However. an hour or so later evidence
often appeared of new echo growth or resurgence. The
physical rrechanism by which a delayed-action response
could occur presumahly involves some l'lynamic process,
or perhaps a microphysical carryover process, but the
details are not clear. Simililr behavior was observed
-31-
in the FACE-l experiment ("oodley et al •• 1982), wtlere
dynamic effeds were an im)ortant par'tOf the conceptual
lllOdel. (It is fair to not~ here that this behavior was
not confi rmed in FACE-2.) These indications warrant
some further investigation.
The NOCMP Operati ons Manual req 11 res the operators to gi ve fi rst
priority to seeding for hail suppreisfon. Consequently. seeding
opportunities for rain enhacement my be missed, or attack.ed late,
because the aircraft are busy with lligher priority activities.
Section 4.2 discussed the responsiv,mess of the NDCMP operators
to hail suppression opportunities.
5. RESULTS OF RADAR DATA MAlYSES
Much of the project effort was devoted to analysis of the radar
data for cl1matological information (already discussed in Sec. 3),
fundamental knowledge. and possible indications of seeding effects.
This section discusses some of the main results of this analysis,
inclUding the deYelopment of relationships between various echo
cluster characteristics.
5.1 Relationships Between Echo Height and Rain Volume
Oynamic effects of seeding have heen indicated in studies of
various randomized cloud seeding projects in the Dakotas (e.g., Dennis
and SChock, 1971; Dennis et al., 1975b). Evidence of dynamic effects
is sought most directly fri-cloud top or maxilllJm radar echo hetght
data. In a randomized experiment. the heights of seeded and non­seeded
cloudS can be cOlTllared either directly or with the aid of a
suitahle cloud oodel (e.g •• SilTllson et a1., 1967) to elucidate any
differences attributahle to the seedTng-:--ln a non-randomized opera­tional
project. such cO"'ilarisons become IlOre difficult. SIllith ~&
(1973) co~ared radar max;lTlJm echo height observations within and
outside the target districts of the South Dakota state weather IOOdi­fi
cat i on project and found some indi cat ions of differences consi stent
with the ell:pected dynamic effects of seeding. However. there were
other possible explanations for the differences and the results were
not conclusive.
One way to establish a physical relationship that is consistent
with the expected dynamic effects of seeding is to examine the rela­tionship
between maxil1lJm echo height and rain volume. The physical
argument begins with the release of latent heat invigorating the
vertical growth of the clouds which, in turn, produce greater rain­fall.
Such an analysis can establish the existence of an echo height­rain
volume relationship but cannot prove that seeding increases
either the heights or the volumes. The results may therefore be
conststent with, without being conclusive of, dynamic effects of
seeding.
The question can be approached in different ways. The low-ti It
radar data acquired on a given scan can be used to determine a volu­metric
rain rate (m3/hr) for each cluster, and tl10se rates can then
be compared with tile observed maximum echo heights (MEH). The height
values can be taken from the same scan. or from a preceding scan in
order to allow for the time requ1red for the precipitation to descend.
Alternatively. the rain volume can be accumulated over the duration of
each cluster and the total amount then co""ared with the maxilllUm ecl10
height found during the entire cluster lifetille. Intermediate ver­sions
are 111 so possible with the cO"'ilarisons being made between rain
volumes and maximum echo heights for some fill:ed time interval, say.
one hour. There are some indications (Oennis et /11 •• 1975a) that
cloud depth (MEH minus cloud base height) mighthelletter correlated
with the rain volumes than is the MEH alone.
-33-
Most of our work was with the MEH and rain volume values for the
entire cluster lifetimes. Values presented from the 1980 and 1981
data in earlier reports (Smith et a1.. 1982, 1983) are suspect because
of questions about the antenna elevation angle data. Therefore we
emphasize here the MEH-rain volume relationship found for the 1982
data (Fig. 23). The rain volumes are plotted on a logarithmic scale
because of the wide range of values involved. Work by Gagin (l980)
suggested that the echo heights could be plotted on a logarithmic
scale as well. In fact. the correlation turns out to be slightly
higher (0.836 vs. 0.833) if that ;s done. The standard error of
estimate of the logarithm of the rain volume is also somewhat lower
(0.61S vs. 0.619) if the logarithmic height scale is used. However.
the differences are so small as to be of little real significance.
Table 5 presents the parameters of the rain volume-MEH
relationship for this data set. There is not iTlJch evidence in
Fig. 23 of a difference between the points for seeded and non-seeded
clusters. so separate regression parameters are not presented. The
DICKINSON 1982
EE
~w
3 3
g
~ 2
~o"
g
'UNSEEOEO
@SEEOEO
" ../:'.....
: :~:: .' (...... ": .
,..' .,.·f
o ..,,
'0 ~ %. ~
.. :.::~ '. 0. • 0
. · .. .'0.·
-1
0
L -~-~--~-~-~-J12C-~~-''-:6-~---:20
MAXIMUM ECHO HEIGHT (km)
~Q: Scatter plot showing the relationship between maximum radar
echo heights and radar estimated rainfall volumes for the 1982
clusters. Twenty-eight clusters were seeded and 323 were non-seeded.
-34-
TABLE 5
Regression Parameters for Cluster
Echo Height - Rain Volume Relationships
(1982 Dickinson Radar Data)
log V log V
vs. MEl~ ~
logarithm of Rain Volume:
Mean 2.084
Standard Deviation 1.118 (same)
Coefficient of Variation 0.54
Maxilrom Echo Hei ght:
Me,n 8.50 km 0.895
Standard Devi at i on 3.31 km 0.178
Coefficient of Variation 0.39 0.20
Regression Parameters:
Correlation Coefficient r 0.833 0.836
r' 0.694 0.698
Intercept -0.312 -2.604
Slope 0.283 I:.m- 1 5.238
Standard Error of Estimate 0.619 0.615
correlation coefficient is about the same as that found in Project
Cloud Catcher (0.85; Dennis et al •• 1975a), and the relationship ;s
consistent with the idea thattiller clouds tend to produce rrore
precipitation.
A linear relationship on a semi-log plot like thdt in Fig. 23
indicates an eKponential relationship between the variables, of the
form
Rain Volume V = A x lQb{MEH)
Values of the intercept parameter log A and the slope parameter b of
this relationship appear 1n Table 5. They indicate that each incre­ment
of 1 km in lM.xiRl,lm echo height produces approximately a m
increase in the cluster rainfall (antl1og of 0.283). Therefore this
basic tenet of the dynamic seeding hypothesis seems to be supported
by the observations from tforth Dakota.
-35-
In the 1981 data. the overall cQrrelation between log V and MEH
was weaker (0.668; Smith et a1.. 1983). That may be due largely
to the possible elevationan'9f'e dat-l. quality problem III?ntioned in
Sec. 2.1. However, when cO"lluted by mnths (June. July. August).
there was a general increase in the correlation over the surrwner
(0.60. 0.71, 0.80). This suggests 'Will! significant variations in
the general weather patterns during the summer of 1981. June was
relatively cool and wet with an abundance of stratiforlll systems
havi ng sorre embedded convect i on. The observed maxiflRJm echo hei ghts
were generally less than 10 km. However. in July and August. the
weather was Il'Ore convective and the majority of systems exceeded
10 km in height. The greater range of MEH's probably contributed
significantly to the increase in thp. correlation.
Another factor that may contri bute to the di fference between the
1981 and 1982 correlations is the way in which we defined the echo
cluster entities. In our classification. a cluster may contain any
number of individual cells. and roost contain more than one cell. This
means that the horizontal dimensions of our clusters are essentially
unrestricted; however, the vertical dimensions are IlOre limited (e.g ••
by the tropopause height). To give an illustration, suppose that a
cluster contains four identical cells. each having an MEH of 10 km.
If one were to define each cell as a separate entity, each cell would
be about one-fourth as large and produce one-fourth as l'IUch rain as
the overall cluster, yet all of the MEH values would be identical. If
every echo cluster contained the same number of identical cells, one
would expect the ·cluster" height-volulII? correlation to be the same
as the ·cell· correlation. The main difference WQuld be that the
clusters would produce rrore rain, by a factor equal to the number of
cells contained. However, if il. single cell dominates the rain volume
for each cluster, this lTIJ1tiple-cell problem would be considerably
moderated.
The average cluster identified in 1981 was si9nificantly larger
than the average 1982 cluster. This may mean that the 1981 clusters
tended to contain rrore cells, on the average. than the 1982 clusters.
which could tend to produce greater scatter in the log V-HEH
relationship.
5.2 Relationships Between the Area-Time Integral and Rain Volume
The echo cluster data were used to study the relationships between
a llEasure of echo size and duration called the Area-Time Integral
(ATl) and the rain volume (Ooneaud et al.. 1984b). The rain volume V
over an area A during the time T is-gfven by
V'~JARdadt (1)
where R is the rainfall rate. If R were a mnstant. Rc ' (1) could
be written as
v • RC~~ da dt (2)
The All Is the double integral in (2). which in analyzing data can be
approximated by a sum:
All '" .Irri da dt .. L: Ai .6.ti A i
(3)
Here. Ai 1s the area over which rain was detected during the i th
observ1ng per10d and .6.t1 is the time interval between observations.
The ATI and the rain volume are given here in km2 hr and km2 fI'Ill.
respectively. These quant1ties are distributed roughly log-normally
(cf. Sec. 3.5).
The ATI concept is useful because it 1ncorporates. in a s1""le
way. information about the areal extent and the duration of the
precipitation events. The ATl calculations can be made for fhed
areas on the ground. as in the work reported 11'1 Doneaud et a1­(
1981). or for roving storm systems (echo clusters) as {i\t~
present work.
With radar data. the value of the echo area at any given time
depends strongly upon the reflectivity factor threshold employed, so
the All values have a simili!lr dependence. The amounts of rain asso­ciated
with regions of low reflectivity are relatively small. however
(see Sec. 3.6). so the rain volumes are lIlJch less sensitive to the Ze
threshold. The choice of the IOOst appropriate reflectivity threshold
for calculating the area-time integrals 1s therefore partly subjec­tive.
After considering the factors discussed in Ooneaud et 031.
(1984b). we settled upon a 25-dBz threshold for determiningt~AlI's
from radar data. at least for the semi-arid type of climate of North
Dakota.
Figure 24 illustrates the fact that scatter plots comparin9 the
cluster rain volumes and area-time integral values expressed on loga­rithmic
scales show strong correlation. The correlation coefficients
and the logarithmic standard errors of estimate are roughly 0.98 and
0.16. respectively. The latter implies a one-standard-dev1ation
scatter in the rain volume estimates of a factor 1.45. In percentage
terms. the corresponding range is between +45 and -31%. That is com­parable
to the uncertainties which typically occur in rain volume
estimates obtained from radar data in the usual manner, employin9 Z-R
conversion followed by space and time integration (e.g •• Atlas. 1964).
The points for seeded clusters have been circled in Fig. 24. One
evident difference between the seeded and non-seeded clusters ;s that
the former tend to be concentrated toward the upper part of the plot.
-37-
Io'r------,----"=c=="-T=---,---,
• NON-SEEDED CLUSTERS (5081
o SEEDED CLUSTERS (75
CORR. COEFF ~ 0.98
101 10 103 10" 105
25dBz-AREA TIME INTEGRAL (km2hr)
~~: Scatter plot and regression line of echo cluster rain
volumes VS. 25 dBz area-time integrals for 1981 Bowman data.
Points for seeded clusters are circled.
This is due largely to a "selection bias" inherent in the NDCMP
operations. where first priority ;s given to seeding the larger
storms for hail suppression.
Figure 25 shows a similar scatter plot for the 1982 radar data
from Dick.inson. The correlation coefficients and regression param­eters
for these plots are quite similar from year to year (and even
from II'(Inth to mnth with; n a 9; yen year). There may be some small
differences associated with climatological variations, but there is
little reason to doubt the general consistency of the ATI-rain volume
relationship. That relationship can be expressed in tile form of a
power law ilS
v " K (AT!)b
with an exponent b that is not far from unity.
-38--
(4)
lOG (ATI)
~~: Scatter plot comparing radar-estimated ra1n volumes from
echo clusters with their area-time integrals (volumes in km2 lTID,
All values in km2 hr). Points for seeded clusters are circled.
Oata frOOl Oickinson. NO, radar, 1982.
To test the consistency of the rain volume versus area-t1me
integral relationship further, the parameters for (4) derived from the
1980 HOCMP radar data were applied to the 1981 echo cluster ATI values
to estimate the corresponding rain volumes. Those estimates were then
cOlllJared with the radar-estimated rain volumes co~uted in the usual
way. using a Z-R relationship to obtain the rainfall rates followed
by space and time integration.
Figure 26 illustrates the results for a sample of the 1981 echo
clusters. The agreement is fairly good, but the least-squares line
(dashed) is inclined slightly to the y ,. X line. The dashed line,
therefore, indicates a slight tendency for the 1980 for11lJla to
overest1mate the 1981 rain volumes for small ATI values and under­estimate
them for large ATJ values. That may be caused, in part, by
the different weather conditions 1n the project area between 1980
and 1981. However, the 1980 data included very few seeded cases, so
the 1980 forlll.lla can be considered as essentially one for un seeded
clusters. The fact that it underestimates the rain volumes for ITI:)st
of the 1981 seeded clusters could then be tak.en to suggest a positive
effect of the seeding upon the rain volumes.
-39-
~ 1Q5,--,-__,-__-,-,_-,-,-,...", 1. .NON-SEEDEO CLUSn RS "
~ 0 SEEDED CLUSTERS
~ 104 - - - REGRESSION LINE
~"
~ 10J
:>
~r'5:l '10
1
'"~ ~ 10I°OO',;'------.J'O';-,---',o"c;----"o,..,--'-'-0':---"0'
~ RAIN VOLUME ESTIMATED FllOM 19BOFORMULA lkm2mm)
~!!: Colll'arison of echo cluster rain volumes c0lll'uted from Z-R
conversion and integration with corresponding volumes estimated from
the 1980 rain volume ys. 25-d8z ATI forllUla. Data from Bowman radar,
July 1981. The solid line is the y • x reference line indicating
perfect agreement, wtli Ie the dashed line is the regression line.
Points are shown for all seeded clusters, but only every other
non-seeded cluster.
5.3 Average Rainfall Rates During Storms
The average rainfall rate, R, for an echo cluster is given hy
the ratio of the total rain volume IV) to the dred-time integral
(ATI),
R • V/(ATI) (5)
If V is 9iyen in km2 l1I1I and the ATI in km2 hI", their ratio has units
of lIIlI hr-1• This ratio is the aye rage rainfall rate oyer the life­time
of the cluster and oyer the are" with rain (Ooneaud et a1.,
1984a). Using radar data, this average value will be senSitive to
the reflectivity threshold used ;n the ATI computation.
-40'
The average rain rate considered over short periods of time (i .e.,
minutes) exhibits large variations during convective storms. However,
if the average is computed over longer time intervals, or particularly
over the lifetime of a storm. it shows truch less variability from one
storm to the next. As the time period included in the computations
comes closer to the total storm duration, the scatter of the average
rainfall rates is reduced.
From (5) and (4) we can obtain:
R:: K(ATI )b-l (6)
Since b .. I, the average rainfall rate R is almost independent of the
size and duration of the storm. as indicated by the ATI. Because the
ATI-rain volume relationship seems to vary little from year to year,
R should also change little from year to year. Thus, a comparison of
cluster rain volumes computed from the 19B1 radar data using a) the
overall average rainfall rate found in 1980; <ino b) the standard
Marshall-Palmer Z-R relationship showed no significant differences.
If b :: I, the cluster average rainfall rate is independent of the
ATI and n is numerically equal to K. Using a 25-dBz threshold for
the ATI calculation. in North Dakota the overall average rain rate was
.. 4 am hr- 1 for a dry season (i .e., 1980) and ~4.8 fill! hr- l for a wet
season (i.e •• 1981). This suggests that the average rain rate may
depend somewhat on weather condit ions.
To get some idea of the distribution of the rainfall over the
cluster lifetime. each cluster was divided into its growing and
decaying periods by considering the radar scan with either maximum
echo area (for Ze > 25 dBzl. maximum reflectivity factor. or maximum
echo height as the dividing point. On the average, a cluster reached
its maximum development after about 56% of the total cluster lifetime.
Separate average rainfall rates were then computed for the growing and
decaying periods. The average rainfall rate for the growing period
exceeded that for the decaying period by an average of ~20% (Fig. 27).
This can be compared to the subtropical climate of south Florida,
where the rainfall rate for the storm growing period was found to
average about twice that for the decaying period (Griffith et a1.,
1978). --
A trultip1e linear regression analysis demonstrated that the radar
estimated rain volume from a cluster is well correlated with the
maxitrurn single-scan volumetric rainfall rate. That suggests the
possibility of estimating the total rain volume for a storm imme­diately
following identification of its maximum stage of development.
This could improve rain volume estimates from satellite data because
larger errors might be encountereo in such calculations due to
overestimation of the rain volumes during a storm's weakening or
decaying phase (due to cirrus dehris).
-41-
-G~OWINGPE~IOD
--DE(:AVINGPEAIDD
•,f----;------t------''''---ii02=:=;,iF-------..J
AVERAGE RAIN RATE (mm hr-')
~ll: The relative frequency distributions of cluster average
rainfall rate for growing and decaying periods.
5,4 Correlations Between Echo Cluster Characterist~
Correlations between several pairs of the identified radar echo
cl uster characteri sti cs were computed. The pu rpose was to i dent i fy
those characteristics that are IOOst closely related to the rain
amounts as well as to explore the extent to which the various
characteri st i cs can be considered independent. Tabl e 6 1i sts the
characteristics included in this investigation, together with average
values for the 1981 (Bowman) and 1982 (Dickinson) data sets. Some
differences are apparent between the two years; they are due IOOstly
to interannua 1 vad aU ons plus the absence of 1ate-summer con vect i ve
storms from the 1982 observations.
The aforementioned possibility of elevation angle errors in the
1981 data may also have contributed to the di fference in average maxi­mum
echo heights. Partly because of that problem, a complete corre­lation
matrix is presented only for the 1982 data (Table 7). As
previously noted, the strongest correlation by far is that between the
area-time integral and the rain volume. However, several other pairs
of variables exhibit good correlations. The maximum reflectivity fac­tor
(ZMX) is well correlated with all of the other variahles. This is
reasonable hecause intense storms tend to be larger. last longer, ilnd
produce IOOre rain.
~4Z-
TABLE 6
Average Values of Some Echo Cluster Characteristics
".Jmer of clusters
Haxlllllm echo height, MEH (km)
Maximum reflectivity factor, ZMX (dBz)
Echo durat ion, T (hr)
Area-t1me 1ntegral, All (km2 hr)
Rain volulIIe. V (km2 1lI1l)
Average rainf1l1l rate, R (rrm/hr)
*Geometr1c mean values.
TABLE 7
5B3
9.85
43.9
1.36
79.8*
350*
4.45
351
8.59
41.2
32.8*
131*
3.50
Correlat10n Coeff1cients Between Echo Cluster
Characteristics for the 1982 Radar Data
log R
log V
10g(ATIhs
ZMX
0.577
0.646
0.833
0.798
0.771
0.578
0.663
0.836
0.798
0.790
-43-
0.820
0.887
0.B56
0.800
0.482
0.565
0.978
0.571
0.616
The good correlation between ZMX and the cluster average rainfall
rate (It') suggests that one could es~imate the average rain rate from
the maximum reflectivity factor wit'l reasonahle confidence. That
fnformation might be used along Wit.l the AT! to get il slightly
improved rainfall estimate by takin'l
v " R(Z) x (AT!). (7)
The good correlation between ZMX and log V is also reasonable. More
intense storms tend to produce IOOre rain, and to calculate Vane rust
use some Z-R formula relating reflectivities to rainfall rates.
The good correlation between ZMX and MEH is also plausible in
light of previous work. The 0.77 correlation found for 1982 is
sli9htly better than the 0.72 obtained for 1981, but that difference
may be related to elevation angle elTors in some of the 1981 data.
Similar height reflectivity correlations were noted in radar obser­vations
associated with operational cloud seeding in South Dakota
(Smith li.!L., 1973), at least for the un seeded echoes.
The sample of seeded clusters in 1982 was small (28 cases), but
some overall comparisons can, nevertheless, be made hetween the seed
and no-seed groups. Table 8 comparf'S the average characteristics for
the two categories; the selection bias. accentuated in these data by
the restrictions on seeding for rain enhancement in 1982, is evidellt.
Tab 1e 9 compares the carrelat ions between v':! ri ous radar vari ab1e pa irs
for the unseeded and seeded groups. Most of the correlations are Il'KJch
weaker in the seeded group. Some of this can be attributed to the
fact that the range of values for the seeded clusters was rather
small, as was the sample size. However, it is encouraging to see that
the main correlations, such as those between rain volume and AT! or
maximum echo height, remain strong pven for the data sub-sets.
5.5 Rainfall Comparisons
To evaluate the effects of seeding upon precipitation, one would
ultimately like to compare the rainfall in the seeded target and
unseeded areas. The present radar data do not permit this because
continuous coverage was not available. However, the radar estimated
rain volumes for the echo clusters were subjected to a seed/no-seed
comparison. Figure 28 shows the frequency distributions of seeded and
unseeded cluster rain volumes (with the latter included whether or not
they were inside a target district) for the 1981 Bowman radar data.
This straightforward comparison of the frequel1cy distributions of
the rain volumes from seeded and non-seeded clusters shows that the
former tend to have considerably greater rail1 volumes. However, that
difference is due largely to the previously mentioned selection hias
operating in the NDCMP: priority is given to seeding larger storms
1,/,.
TABLE 8
Compar1son of Average Values of Some Characteristics
of Seeded and Non-Seeded ECh'O Clusters (1982 Oata)
Seeded Non-Seeded
Number of clusters 28 323
Haxiflllm echo height. MEH (km) 13.3 8.2
Haxirum reflectiv1ty factor. ZMX (dBz) 51.1 41.0
Area-time integral. AT! (km l hI") 562.3* 25.6*
Rain volume. V (kmZ urn) 2965* 100*
Average rainfall rate. R (nInthI') 4.71 3.40
*Geometric mean values.
C- _
for hail suppression. A greater proport1on of the l"rger. more
intense clusters 1s therefore seeded and the apparently greater rain
volume is mainly a consequence of that selection process. Conse­quently.
the selection bias renders this type of comparison invalid
for estimating the effects of seeding on rainfall. The only conclu­sion
one can glean from Fig. 28 is that the NOCMP seeding operations
are. indeed. carried out in the manner prescribed in the Operations
Manual. that is. by giving first priority to seeding the strongest
storms for hail suppression.
An attenvt was made to circumvent this selection bias by
class1fy1ng the clusters as in or out of a target district. Clusters
passing across any part of an NDCMP district at any time during their
life histories were classified as "in-district." The thought was that
all of the clusters in a district should be candidates for seeding.
while those clusters outside the district are not SUbject to treat­ment.
The NDCMP operators can be presumed (at least under idealized
conditions) to treat the in-district clusters in the manner roost
appropriate for enhancing the in-district ra1nfall. If this presump­tion
were valid. one would have a reasonable datil set to which target­control
statistical co""arisons could be applied {assuming. of course.
that seeding 1n the district would oot. or did not. influence the rain
-45-
TI\BLE 9
COqJarfson of Correlation Clefficients Bet~een Echo C1'lster
Characteristics. for See:led and Non~Seeded Clusters
(1932 Data)
Variables ... ~ ~!l ...l!1L log{ATlhs ~
R 0.428 0.425 0.652 0.187 0.287
(0.567) {0.566l (0.829) (0.479) (0.572)
log R 0.476 0.474 0.759 0.116 0.352
(0.643) (0.6541 (0.891) (0.562) (0.617)
log V 0.797 0.786 0.605 0.980
(0.808) (0.813 I (0.854) (0.975)
log(ATI)2s 0.765 0.763 0.611
(0.768) (0. nIl (0.791)
ZHX 0.555 0.560
(0.767) (0.778"
*Top nUl1ber. seeded clusters; bottom nU'Ilber (in parenthesesl.
non-seeded clusters.
production outside the district). -f this presumption is not valid.
the main effect would be to dilute lhe apparent effects of seeding in
any in- versus out-of-district cO~irison.
Such a cOqJarison of the cluster- ra1n volumes shows a tendency
toward greater ra i nfa 11 from the in-di str; ct cl usters (Fi g. 29).
However, it appears that another kird of bias may be mainly respon­sible
for that apparent difference. Small echo clusters will be
distributed over the map rore or less at random. and therefore (after
appropriate adjustment for the area5 involved) would be about as
likely to fall within as outside a district. Large clusters. on
the other hand, tend to have longer lifetimes and cover !OOre area
as they move. They are therefore more 1ikely to cross over some
part of a district. Consequently. the larger clusters are rrore
likely to be placed into the "in-district· category. leading to a
bias in the in- versus out-of-district cOfl)arisons. That
"classification bias· therefore renders this approach to the
rainfall comparisons invalid as well.
-46-
SEEDED
CLUSTERS "----'r-1751 , ,
,,,' \,
" \
I \
\
,
,,
101 !if 103 104 105
RADAR ESTIMATED RAIN VOLUME (km2 mml
~~: Co~arlson of frequency distributions of the radar estimated
rain volumes for seeded and non-seeded echo clusters. for the 1981
Bowman data.
trwr
301---.------';~~~~~~'---_r--__,
ro
rw
> 10
~g ~~: Co~arison of frequency distributions of the radar estimated
rain volumes for in-district and out-of-dfstr1ct echo clusters; 19iH
District t (Bowman) data.
-47-
These approaches, while of interest, have therefore failed to
yield acceptable evidence about possible effects of seeding on the
precipitation. The comparisons are not inconsistent with the intended
effects of the seeding, but the selection and classification bias
probl ems prevent drawi ng any substant i ve inferences. An al ternat i ve
approach woul d be to use long-term tot a1 ra i nfa 11 compari SOr'lS, but the
mode of radar data acquisitior'l used in this investigation is not
sui tab1e for such compari sons because the coverage was not cont i nuous.
Therefore such work wi 11 have to be done wi th the ai d of avai 1able
rain gage data from the project area.
5.6PossibiJ..!..1.t.Q.f.~~.i!ltheObservations
With a 20 antenna beamwidth, questions may arise about possible
range variations of the radar rreasurements (e.g., Wilson, 1975;
Collier, 1984; Zawadzki, 1984). Even though data were recorded out
to about 275 km range, the North Dakota radar data were analyzed
only to a range of 145 km. That mitigates some of the difficulties
encountered at longer ranges.
To investigate possible remaining range effects, we used several
procedures. Fi rst, the maximum equi va 1ent refl ect i vi ty factor data
for the echo clusters observed at Bowman in 1981 were divided
according to whether the observations occurren "near" or "far" from
the radar. The di vi di ng poi nt was taken as r '" 105 km, whi ch di vi des
the useful area of radar data into two approximately equal parts.
Figure 30 shows frequency distributions of the maximum reflectivity
factors for these two subsets of the data. The di fferences are not
large, but there is a general tendency for the observed maximum
reflectivity factors to be smaller for the more distant echoes. Such
a difference could be explained by beam filling problems at long
ranges, with occasional contributions due to attenuation by inter­vening
storms. This finding suggests that any results involving the
refl ecti vity factor observat ions shaul d be vi ewed with some cauti on.
Second, "Ie prepared a "two-dimensional frequency distribution" of
observed cluster maximum equivalent radar reflectivity factors (ZMX)
as a function of range from the radar. Figure 31 shows this distribu­tion.
The decrease in radar sensitivity with range is evident because
the minfllllm ZMX values tend to increase with increasing range. In
addition, the behavior at the top of the plot shows that the maxirrum
ZMX values decrease with range. For exa~le, no values .. 60 dBz were
observed beyond 120 \::m. One may be tempted to extrapolate Fi g. 31 to
infer that at some sufficiently long range (perhaps around 250 km) all
of the ZMX observat ions wi 11 be about the same, i.e., around 40 dBz.
These fi gures show that there are range effects on the ZMX
measurements related to the change in radar sensitivity and in
beam-filling considerations with increasing range. These
observations suggest that:
-48-
~~~
RANGE >105 km , ....
(226 CLUSTERS) ,/
/
,/
I
I
I RANGE < 105 km
I (351 CLUSTERSl l
100r----,--.-T=T=-r-=i:?"~t::"'_,.-__,
190
~ 80
~ 70
2 60
~ 50
~40
;::
~ 30
~ 20
u 10
~~: ColTJtlarison of frequency distributions of the maxil1l.lm
reflectivity factors for nearby (I' < 105 km) and IOOre distant
echo clusters; 1981 Bowman data.
60 80 100 120 140
RANGE (km)
40
0 1 2 1 0 0 0 0 0 0 0 0
1 3 5 3 1 2 3 1 2 1 0 0
1 ,. 3 4 1 1 • • 4 5 5 2
2 14 • 2012 11 11 14 14 • • •
5 • 12 • 14 13 • 10 ,. 14 14 14
4 3 1 12 10 13 15 20 ,. 11 19 25
3 3 1 • • ,. 15 12 18 ,.17 14
2 5 8 • 1 • 12 11 22 13 ,.8
0 2 2 5 5 3 • 4 13 3 4 4
0 2 0 1 1 1 0 0 0 0 0 0
20
20
f.!..9.:.11: Two-dimensional frequency distribution of maxilllJll\ equivalent
radar reflectivity factors for 857 echo clusters as a function of
range from the radar (l981 Bowman and 1982 Dickinson data).
-49-
1) Project flJ2'teorologists should not rely on the radar for
detection of initial precipitation at long ranges.
2) Distant storm intensities ",ill he underestimated, which
may lure the meteorologists into an erroneous assessment
of the threat that the storms pose.
Third, we compared the distributions of reflectivity factor values
in the low-tilt data for the in-district versus out-of-district
cluster classifications. To account for range variations in the bin
size in the data grid. the distributions were determined with respect
to echo area. Figure 32 shows the distributions for the 1981 Bowman
data. when the radar was located within District I. A tendency for
more of the echo area for the nearby in-district clusters to be asso­ciated
with low reflectivity factors is evident. The explanation lies
in the greater sensitivity of the radar set for echoes at short range.
This factor may contribute. in a small way, to the in- versus out-of­di
stri ct di fferences noted in Fi g. 29, but the cl ass i fi cat ion bi as
is still likely to be the major contributor.
In 1982. the research radar was located at Dickinson for a variety
of reasons, including the desire to get fOOre nearly equivalent obser­vations
of in- and out-of-district clusters. The areal distributions
E!.!h.E: Comparison of cumulative frequency distributions of
reflectivity factor values based on areal coverage for in­district
and out-of-district clusters; 1981 Bowman data.
of reflectivity factors for 1982 appear in Fig. 33. Evidently the
relocation of the radar was successful in this respect, because the
difference hetween the distributions has essentially disappeared in
the 1982 data.
Finally, to investigate possible range dependence of the maximum
echo height (MEH) observations, we divided the observation area into
10-km range increments and examined the MEH observations from each
10-km annulus (Fig. 34). The upper limit on the scan elevation angle
obviously restricted the MEH obServations for nearby storms (the upper
limit was usually 12° in 1982, but on a few occasions scans were
recorded up to 15°). Apart from that, there does not seem to be f1lJcll
systematic variation of the MEH observations with range. This
suggests that IOOre confidence can be placed in results based on
the echo top observations.
5.7 Oevelopment Q!. Climatological Z-R Relationships for~
Storms 1!!. the Northern Great Plains
The presence of hall in most strong sumlTer-type convecti ve storms
of the northern Great Plains complicates attempts to estimate precipi­tation
from radar data. Hailstones are usually intermingled with
raindrops in the high reflectivity regions of these convective storms
1982 DICKINSON DATA
~
~80
or~40
>
~
~20
8
o'--'-....L-'_:'---'----'-_'--'-----'----'-_'---'
10 10
~~: COllllarison of cumulative frequency distributions of
reflectivity factor values based on areal coverage for in­district
and out-of-district clusters; 1982 Dickinson data.
-:"1-
1982 DICKINSON DATA
NUMBER OF OBSERVATIONS
~
(/J
:;;
~
....
J: "iii
J:
o
J: "W
'::":>
:;;
~
:;;
N ""352
15"/
/
" A
/
10 ~ ~
MEANf..
, /
II MI'N
RANGE(km)
~~: Graph showing observed cluster max;l1l.Im echo "'eights as a
function of range from the radar.
and current operational radars cannot distinguish between the two.
Radar estimates of rainfall amounts from such storms therefore are
often found to be excessively high. A study was therefore made to
develop a convective Z~R relationship for the operational S-cm radar
systems used on the NOCHP by employing the climatological approa"'l
developed by Miller (1971). Calheiros and Zawadzki (1983) used
"non-sil1lJltaneou5 radar and rain gage rainfall data" in a similar
way to develop a reflectivity-rain rate relationship for Brazil.
Smith et a1. (1975) have developed a regiona1 Z-R relationship
intended ~account for the effects of hail by collllaring lO-cm
radar data collected on the North Dakota Pi lot Project (NDPP)
wi th gage data us 1ng 11 di fferent opt i mi zat ; on approach.
Only equivalent radar reflectivity factor values> 42 dBz were
considered in this analysis. because it focuses on situations where
hail is likely to be present. A threshold of 42 dBz corresponds to a
rainfa 11 rate of about 15.2 nrn h-1 us lng the well-known Z '" 200 RI. 6
relationstdp. ThuS. smaller rainfall rates and Ze vallJes were not
considered in constructing the cUlllJlative frequency didgrdms found
herein.
-'Jz-
The vertical section of a shower rer>resented by a 2° beamwidth
radar observation is comparable to that sampled during a 5-min surface
rainfall observation. Sho~lers usually occur with weak to moderate
downdrafts. and the rain (with hail intermixed) typically falls at
an average speed of about 10 m S-l. At this rate •. the precipitation
will fall about 3 km in 5 min. A 2° radar beam is about 3 km wide
at -85 km distance. Thus the radar observations can be considered
reasonably comparable to 5~min rainfall events.
In 1982. volume scans of convective storms were made at
approximately 6-min intervals using the 5-cm radar system located
at Dickinson. The radar reflectivity factors> 42 dBz. observed at
the 1° elevation angle for ranges between 60 and 105 km, were tabu­lated;
this range interval was chosen to minimize problems with ground
clutter and the range effects discussed in Sec. 5.6. Figure 35 shows
a cumulative frequency plot of the Ze values thus obtained.
During the period 1957 through 1970, the Newell (SO) Agricultural
Research Station maintained several 6. 12, 24. and 192-h recording
rain gages in conjunction with watershed sturJies (Fig. 36). Data from
90
60
70
60
50
40
30
20
"
~ ~ M
RADAR REFLECTIVITY FACTOR, Ze (dBz)
99
98
97
93
92
"
~~: Cumulative frequency distribution of 5-cm equivalent radar
reflectivity factors> 42 dRz ohtained during 1982 in southwestern
North Dakota.
_5J~
1982
I DI~~~;~)N ~
I 0 • BISMARCK I 1970 ~
I LEMMON ~
~ ~~~__ ~ __N.DA~.:.-
I 0 'E S.DAK.
I ~
~.9J::!.1:.~ ;'."\ ~
WyO. r • 1 !NE~El~ PIERRE
IRAPID CITY
RADAR SITES
NEWELL AGRICULTURAL RESEARCH
STATION WATERSHEDS
o 40 80 160
KILOMETERS
.E..1Jk1!: Map showing the locations of the Dickinson radar site and
the Newell Agriculture Research Station's (ARS) watershed studies,
Radar data from the Lemmon site were used in an earlier study
(Miller, 1971),
several of the gages were selected for analysis. Ei9hty-five statiorl­seasons
of summertime (May through August) 5-min rainfall rates were
tabulated. A total of 1731 rainfall rate values which equaled or
exceeded 0.05 inches per 5-min period (~15.2 fTlll h- 1) were used to
construct the cumulative frequency distribution shown in Fig. 37.
A Ze-R curve was then generated by selecting radar reflecti vity
factors and rainfall rates for corresponding points on the appropriate
cunulative frequency distribution curves. For example, in Fig, 35,
50 dBz is the value below which ~91.8% of the observed reflectivity
factors lie. The rainfall rate distribution, Fig, 37, was entered
at the same frequency level to find a corresponding rainfall rate of
63 om/h. Continuing to plot Ze values against corresponding R values
in this way resulted in a number of points. which were then used to
obtain a least squares Ze-R equation, The rainfall rate (R) was
treated as the predictand using the equivalent radar reflectivity
factors (Ze) as the predictor in establishing the least squares
regression line,
The resulting equation is:
(8)
-54·'
CF%
100
90 99
80 98
70 97
60
95
40 "
30 93
20 92
10 " L.L-L--L-l_l-LL-'----l~.L..L---"---,JOc_-'-"""-.l.~fo_l90
20 40 60 80 100 120
FIVE MINUTE RAINFALL RATES (mm h-')
~E: CUlllJlative frequency distribution of 5-min rainfall rates
observed over the period 1957-1970, using data from the Newell ARS
site (Fi g. 36).
This 5-cm relationship is very close to the well known and widely
used Z = 200 Rl.6. It fails to show 11 crossover between the common
all-rain (Marshall-Palmer) and all-hail (Douglas) relationships as
was found by Miller (1972) and Smith ~~ (l975) using la-em radilr
data. This is likely due to the fact that very high Ze values are
less frequently observed using a 5-cm wavelength because of
attenuation.
These results support the use of Ze = 200 RI.6 in using 5-cm
radar data for estimating convective rainfall in the northern High
Plains. For a lO-cm radar system, however, a different relationship
may be needed; Ze = 200 RI.6 may be used for Ze values < 42 dl1z and
Ze = 25 R2.37 for values> 42 dBz, as suggested in f1i ller (1972).
Alternatively, the single optimized relationship in Smith et al.
(1975) may be used. ---
Table 10 compares rainfall rates calculated for selected Ze
values from the well known Marshall-Palmer forl1l.l1a, the 5-cm
equation developed herein (8), the optimized equation developed for
thi s regi on by Smith et a 1. (1975). and the two-part equat i on from
Mi 11 er (l972). The presence of ha i l. whi ch produces hi gh Ze va 1ues,
is climatologically accounted for with 10-ern radar data by using an
equation developed for the specific area for the convective storm
season. At 5 cm. the equation <'loes not work in the same manner. but
the signal attenuation tends to reduce the occurrence of extremely
high radar reflectivity values.
-55~
«
0
0 0: :J, 0
N i~
0 ~ ~~
&
~ ~ .
- -E ::: :J\ " E·.
~ ~
~g
<5
!. « 0
0 lZ 0 " E~ ~ - u~ ~ f~
~ ] ! «
. "0 ~ - 0 ti ~
j
~ I ~.-
.0_> L
-;;;t:B 'll 'll '6 .;~~
"I :ll :g
Jf~
-56-
This approach to developing Ze-R relationships could be applied
to radars w1th narrower bearrw1dths (e.g •• 1°) by cOlJllarlng the Ie
distributions with 2- or 3-m1n rainfall rate c11matologies. Further
studies using the techniques of Calheiros and Zawadzki (l983) con­cerning
the variation of equivalent radar reflecthity cUIlRJ1athe
probability curves as a function of distance from the radar may
further improve this climatological approach.
-57-
6. NUMERICAL CLOUO MODELING STUOIES
Ewald (1983) evaluated the dynamic seedahi lity of clouds in North
Dakota usin9 the 1500 MDT sounding data taken in 1981 at Baker,
Montana, in connection with the CCOPE project as input to the GPCM
one-dimensional, steady-state cloud lTOdel. (Due to a misunderstanding
about the way the sounding data were archived, the times were erro­neously
reported as 1500 GMT by Ewald and in Smith et al., 1983.)
Changes in cloud top height (bH) and in maximum updraftspeed (lIW)
between natural and seeded versions of the simulated clouds were
determined. Results from the analysis of the "'H values indicated
about the same degree of potential for dynamic seedabi lity as had been
found in earlier studies in North Dakota (Dennis et al.. 1975b). On
some days, the seeding of larger clouds produced larger increases in
cloud height, while on other days, the smaller clouds showed ITIOre
seeding potential. The tlW analysis indicated frequent occurrences
of increases of a few meters per second in updraft speed, with
larger clouds showing the lTOst promise for seeding according to
this criterion.
6.1.1 Analysis 2.!.. growth itl~!.QQ. height 11981 ~
The earlier Dennis et al. (l975b) analysis of dynamic seedability
in North Dakota clouds wast>ased on morning soundings (1500 GMT). It
is possible that capping inversions appear IOOrp. often in the morning
soundings, while the atmosphere is hetter mixed hy midafternoon
(1500 MDT), so that greater potential for dynamic seeding would he
indicated in the 1500 GMT data. Unfortunately, few 1500 GMT soundings
are available from Baker, but a similar analysis was carried out using
the 1981 Baker data for the closest time that had numerous soundings,
which was 1300 MDT (1900 GMT). Note that soundings were taken at
Baker only on days when convective activity was anticipated, which
involved ahout half of the total number of days, so the results are
not characteristic of all summer days in that region.
Frequency distributions of the rodel cloud top growth (i\H) due
to seeding were plotted for initial [f(Idel updraft diameters of 1, 2,
3, 4, 5. and 6 kilometers. Table 11 summarizes the computed hH values
as il function of updraft size. Some growth, indicated by a positive
"'H value, was found for at least one of the updraft sizes on 26 out
of 37 days analyzed. Figure 38 shows the frequency distrihution for
the maximum "'H value found for each day, while Fig. 39 shows a I'.H
distribution for the 5-km diameter updrafts.
Using these 1900 GMT soundings. the potential for dynamic
seeding effects appears to be somewhat greater than for the 1500 MOT
-58-
TABU: 11
Hodel-Corrputed lIH Values I'm); 1300 HOT (1900 GIl)
Bal:.er, Hontana, 19B1 Sounding Data
------------------------------------------------------------------------
Updraft Diameter (km) Maximum Tropopause
Date 1,0 2.0 3.0 4.0 5.0 6.0 -~ He' ht 'm
5/21 0.6 2.0 0.5 0.2 0.5 0.3 2.0 12.4
5/22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10.3
5/23 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11.1
5/25 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \l.S
5/26 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12.2
5/28 0.0 0.0 0.0 0.0 0.3 1.0 1.0 11.8
5/29 1.0 .b..!. 0.0 0.0 0.0 0.0 1.9 12.2
6/1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12.4
6/3 1.8 0.3 0.3 0.3 0.0 D•• 1.8 12.2
6/6 U D•• 0.3 0.3 0.0 0.0 0.5 12.6
6/7 0.0 0.0 0.0 0.0 0.0 0.0 0.0
6/' 1.3 0.5 D•• 0.2 0.1 0.3 1.3 11.4
6/10 o.s 0.3 0.5 0.5 0.3 0.7 OJ 12.4
6/11 OJ 0.5 0.3 0.3 0.5 "IJ.5" 0.7 13.2
6/12 0.0 0.0 D.' D.' 0.3 0.0 D.' 13.3
6/13 0.0 0.0 TI "IJ.5" 0.6 0.6 3.1 10.8
6/16 0.0 D.' u.s- 1.3 I.' Ll I.' 11.5
6/18 0.0 0.3 o.r 0.2 10 0.3 0.3 10.8
6/23 0.5 1J.4 0.7 0.5 0.3 10 0.7 11.6
6/27 D.' 0.5 0.0 0.3 0.0 0.0 0.5 15.1
6/28 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11.3
6/30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13.1
7/1 2.3 0.0 0.0 0.' 0.3 0.0 2.3 15.2
7/6 0:0 0.2 0.' 0.3 0.0 D.' 0.' 15.3
7/7 0.' 0.3 10 D.' 0.' 1J.4 0.' 15.7
7/10 U 0.0 0.3 0.3 0.0 0.0 1.3 15.2
7/11 U 0.5 0.3 0.3 0.0 0.0 0.5 15.2
7/12 0.0 lG 0.8 1.0 0.' 0.6 1.0 15.2
7/13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15.2
7/17 0.5 0.6 0.8 0.7 0.3 0.3 0.8 15.1
7/18 0.2 1.0 T.4 0.2 0.3 0.6 2.' 14.7
7/1' 2.3 0.0 0:0 0.0 0.3 0.0 2.3 11.1
7/20 0,2 I.' 0.' 0.5 10 0.3 I.' 11.7
7/27 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12.0
7/2' ~ 0.5 0.2 0.0 0.0 0.0 0.6 15.1
8/2 0.0 0.' 0.0 0.3 0.3 0.0 0.' 11.8
8/' 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15.1
Mean 0.42 0.35 0.36 0.26 0.19 0.22 0.81
-59-
".~-------------------~
1981 BAKER MT - 1300
!O
,
I1AX.IMUMOfLUH (k.lIl
~~: Frequency distribution of max1mufll rrodel predicted cloud top
growth (.II.H) expected due to seeding using 1300 MDT (1900 GMT) 1981
Baker sounding data as fnput.
1981 BAKER "'T - 1300 MOT
.5 !.5 2.5
IJElTAH!k-.l
f.!.L.~: Frequency distribution of rodel predicted lIH values due to
seeding for 5-km diameter updrafts j 1300 MDT Baker 1981 soundings
used as input.
-60-
soundings. The median maximum AH (Fig. 38) is ahout 0.6 km, and ahout
one-third of the days with soundings have maxilllJm b.H values greater
than 1 km. The figures in Table 11 suggest that most of the potential
for dynamic seeding was associated w1th the smaller clouds (updraft
sizes up to about 4 km). In fact, on about half of the days with any
growth potential at all the greatest b.H values occurred for the
smallest nndel clouds (updraft sizes lor 2 kill). Other studies have
also suggested that such clouds represent the best potential for rain
increase seeding in the High Plains (see Dennis ~.!h., 1975a).
Dennis et al. (1975b) suggested that in the North Dakota Pilot
Project, a "d"ayhad good dynamic seedability when the I1y.H1el-indicated
ll.H (based on the 1500 GMT sounding) was greater than 1.2 km for any
of the updraft sizes. If this criterion is used for the NOCMP data,
11 of the 37 days for 1981 would have had good dynamic seedabtlity
according to the rodel analysis of the 1300 f4JT soundlngs. Sl!Iith
et al. (1982) suggested that a day with some AH values greater than
n-:-skiii" could be classified as a good seeding day. On that bas1s,
20 of the 37 days showed some promise for dynam1c seeding.
The median and lrean clOUd top growth values (Table 12) tend to
favor the smaller clouds as well. The clouds w1th l-km updrafts
showed the greatest mean growth. The hi ghest values of AH occurred
for clouds with updraft s1 zes of 3 km or less, and the most frequent
occurrence of AH values greater than 1 km was for 1 and 2 km updraft
diameters.
TABLE 12
SUrmldry of llH Values; 1981 Baker, Montana
1300 HOT (1900 GMT) Sounding Data
Updraft Days With Days Wlth Positive b1i (km)
Diameter (km) Zero 6H Positive AH Median Mean
1.0 19 18 0.50 0.87
2.0 17 10 0.50 0.64
3.0 17 20 0.30 0.65
4.0 15 22 0.30 0.43
5.0 19 18 0.30 0.40
6.0 21 16 0.30 0.51
-61-
6.1.2 Analysis.Q.f.. increases .i.!!..!!£S!Ei.!.!.~~~
The change in maximum updraft speed associated with seeding (llW)
was found by taking the difference between the mdel calculated values
of the maximum updraft speed in the simulated seeded and natural
clouds. Frequency distributions of llW were plotted and analyzed in a
manner similar to that used with the llH values. About two-thi rds of
the days showed some increase in maximum updraft speed for some
updraft sizes (Fig. 40). For those days with a positive ll.W, the
median maximum ll.W was about 3 m S-I. In every case, the maximum I'lW
occurred for the larger clouds (updraft diameters 3 km or greater),
and on half those days with some ll.li values greater than zero, the
maximum occurred for the largest updrafts (6 km diameter). Table 13
shows that the amount of the increase in updraft speed also tended
to increase with the size of the initial updraft.
It is of interest to compare the results for the 5-km updraft
diameters in the NDCMP mdel runs with the corresponding I'lW values
for the same size updrafts obtained for the earlier Rapid Project
(Table 14). As Table 14 shows, il greater percentage of the NDCMP
days had positive llW values; 26 of 37 days (70%) had positive lI.W,
while only 56 of 98 days (57%) had positive I'lW in the Rapid Project.
Part of that difference may be related to differences in the criteria
for deciding when to take soundings. However, the mean and median
llW values for the two projects were rather simi lar.
10
2 3
DELHoNINIlAKlJPORAFT IflI/ser.)
~.iQ.: Frequency distribution of cloud model prp.dicted increil~es
in maximum updraft speed (llWj due to seeding; 1300 MOT Baker 1981
soundings used as input.
-62-
TABLE 13
Mode l-Computed lIW Values (km); 1300 MDT (l900 GMT)
Baker. Montana, 19B1 Sounding Data
------------------------------------------------------------------------
Updraft Diameter '-'-"'-J,.o-~ Maximum
Date 1.0 2.0 3.0 4.0 ~
5/21 0.9 1.8 1.3 2.3 3.1 3.9 3.9
5/22 0.0 0.0 0.0 0.0 0.0 0:-0- 0.0
5/23 0.0 0.0 0,0 0.0 0.0 0.0 0.0
5/25 0.0 0.0 0,0 0,0 0,0 0,0 0,0
5/26 0.0 0.0 0.0 0,0 0.0 0.0 0.0
5/28 0.0 0.0 0,0 0,0 0.0 0.0 0.0
5/29 0.0 0.0 1.6 ~ 2.5 2.2 2.6
6/1 0.0 0,0 0,0 0.0 0.0 0.0 0.0
6/3 0.6 2.3 2,5 2.3 2.3 2.4 2.5
6/6 0,1 0,0 D.3 1.4 2.7 2.6 2.7
6/7 0.0 0,0 0,0 0.0 0:0 0.0 0.0
6/9 0.0 0.4 2.2 3.0 3.3 3.6 3.6
6/10 0.2 1.5 1.7 2.0 2.0 T:9 2.0
6/11 0.2 1,8 2,3 T.9 IT 3.4 3.4
6/12 0.0 0.0 2.3 3.0 3.0 1:4 3.4
6/13 0.0 0.0 3.4 3.9 4.5 U 4.6
6/16 0.0 0.0 0.1 0.0 0.0 1JJf 0.1
6/18 0.4 1.1 TI 1.2 1.2 1.2 1.2
6/23 0.4 0.8 1.4 2.0 2.2 D 1.3
6/27 1,2 0.9 0,8 2,2 2.7 U 2.7
6/28 0.0 0.0 0.0 0.0 0:0 0,0 0,0
6/30 0,0 0,0 0,3 2.1 .?.2 2,2 2.3
7/1 0.0 0,1 2,2 2,9 2.3 2.3 2.9
7/6 0.0 4.5 4.7 3.9 4.0 4.0 4.7
7/7 2.6 2,5 D 3.1 3.3 3,2 3.3
7/10 0.2 1.7 2.7 2.7 2.i 2.7 2.7
7/11 0.0 0.0 0:0 2.0 T.9 T.O 3.0
7/12 0.1 0.0 0.0 0.0 0.7 T.T 1.7
7/13 0.0 0.0 0,0 0,0 0.0 07) 0.0
7/17 0.0 0,0 0,6 1.5 1.9 1.9 1.9
7/18 0.0 0.2 0.5 1.3 IT U 4.4
7/19 0.0 0.1 2.4 2.8 2.5 U 2.8
7/20 0.3 0.7 0,9 T.9 3.3 3,4 3.4
7/27 0.0 0.0 0.0 0.0 0.0 0:0 0.0
7/29 0.0 1.6 2.0 1.1 2.5 1--2. 2.9
8/2 0.0 1.9 2.3 2.8 2.9 2.6 2.9
8/4 0,0 0,0 3,1 ~ IT 3.1 3.9
Mean 0.19 0.65 1.23 1.67 1.89 2.00 2.10
-63-
TABLE 14
Summary of l1W Values; 1981 Baker, Montana
1300 MDT (1900 GMT) Sounding Oata*
Updraft Days With Days With Positive lIW (m/s)
Diameter (km) ~ Positive llW Median Mean
1.0 25 12 0.3 0.60
2.0 20 17 1.B 1.41
3.0 12 25 2.3 1.82
4.0 12 25 2.9 2.47
5.0 11 26 3.1 2.70
6.0 11 26 1.7 2.85
1966-1968 Rapid Project Data
5.0 42 56 2.3 2.36
*Including comparison with Rapid Project results.
The results from this set of model runs (and those of Ewald, 1983)
indicate that the larger clouds show greater potential increases in
updraft speeds due to seeding. Increased turbulence and mixing within
the clouds and between the clouds and their environment provide one
possible explanation for the changes in llW. This behavior suggests a
mechanism for invigorating larger clouds and perhaps increasing their
precipitation, even though increases in cloud top hei ght rray be less
likely.
6.1.3 Model analysis.Q.!. 1982 Dickinson soundings
Soundings were taken at Dickinson during the 1982 field project
on only 18 days. Tables 15 throu9h 17 summarize results of the GPCM
model analysis of those soundings. Note that these tables include
-64-
TABLE 15
Hodel-Co~uted AH Values (km)
1982 Dickinson Soundlngs
-----------------------------------------------~------ ------------------
Updraft Diameter (km) Haxll1l1l'1
Date/Time (GMT) 1.0 2.0 3.0 4.0 5.0 6.0 ~
18 June/1600 0 0 0.1 0.1 0 0.1 0.1
19 June/1500 0.2 0.' 3.5 3.8 4.1 4.1 4.1
23 June/OOOO 0 0 0 0 0 0 0
23 June/1500 0 0.' 0 0 0,4 0 0.'
23 June/1800 0.2 0.2 0.5 0.3 0.3 0.3 0.5
23 June/2IOO 0.7 '.2 0.3 0 0.' 0 4.2
24 June/OOOO 0 0 0.2 0.2 0.3 0.5 0.5
24 June/1500 0 0 0 0 0 0 0
25 June/1500 0.6 0.5 0.8 0.6 0,' 0.5 0.8
26 June/lSOO 0 0 0 0 0 0 0
26 June12100 0 3.9 0 0.3 0 0 3.9
27 June/15DO 0.2 0.3 0.7 2.6 2.9 3.2 3.2
28 June/1600 0 0 0 0 0 0 0
28 June/18DO 0 0 0 0 0 0 0
29 June/l100 2.7 0 0 0 0 0 2.7
30 June/lSDO 0.' 0.2 0.4 0.' 0.2 0.2 0.'
1 July/ISDn 0.' 0.6 0 0 0 0 0.6
2 July/OOOO 0.3 0 0 0 0 0 0.3
2 July/lSOO 0 '.5 0.3 0.3 0 0 '.5
3 July/lSOO 0 4.1 0.3 0 0 0.3 4.1
4 July/lSDO 0 0 0 0 0 0 0
4 July/2200 0 0 0 0 0 0 0
5 July/OOOO 0 0 0 0.' 0.5 0 0.5
5 July/lSDO 0 0 0.3 0.3 0.3 0 0.3
6 July/lSOO 0 0 0 0 0 0 0
7 July/lSDO 0 0 0 0 0 0 0
a July/lSOO 0.2 0 0 0 0 0 0.2
a July/laoa 0.6 0 0 0 0 0 0.6
a July/2100 0 0 0 0 0.' 0 0.'
g July/DODO '.' 0.3 0 0 0 0 4,'
g Julyl16DO 0 0 0 0 0 0 0
Heao 0.35 0.63 0.24 0.30 0.33 0.30 1.19
-1>5-
TABLE 16
Summary of Max1Jwm AH Values
1982 Dickinson Soundings
----_.._-----------------------------------------------------------
Updraft Tropopause
-!!ill.... Time Max AH Diameter TGMTT \kiiiJ \kii) ~,.
18 June 16 0.1 9.9
19 .Alne 15 4.1 11.6
23 June 00 0 11.8
23 June 15 0.' 12.8
23 June 18 0.5 12.9
23 June 21 '.2 12.9
24 June 00 0.5 13.2
24 June 15 0 12.0
25 June 15 0.8 12.4
26 June 15 0 12.8
26 June 21 3.9 13.2
27 June 15 3.2 14.0
28 June 16 0 13.3
28 June 18 0 12.5
29 June 17 2.7 12.9
30 June 15 0.' 14.3
1 July 15 0.6 13.3
2.A1ly 00 0.3 12.7
2 July 15 '.5 15.5
3 July 15 4.1 12.2
4 July 15 0 12.9
4 July 22 0 12.8
5 July 00 0.5 13,2
5 July 15 0.3 13.1
6 July 15 0 11.1
7 July 15 0 11.5
8 July 15 0.2 12.1
8 July 18 0.6 11.7
8 July 21 0.' 11,8
9 July 00 '.' 12.8
9 July 16 0 12,8
-66-
TABLE 17
Increase in MaxhllJm Updraft Speed (AW) Due to Seeding
1982 Dickinson Soundings; 5-km Updraft Oialll!ter
Date/Time 6W Trii/Sl-
18 June/1600 0.0
19 June/1500 0.2
23 June/OOOO 1.3
23 June/1500 1.5
23 June/1800 3.4
23 June/2iOO 2.2
24 June/OOOO 0.0
24 June/1500 0.0
25 June/ISOO 3.2
26 June/lS00 0.0
26 June/2100 3.3
27 June/1500 0.0
28 June/1600 0.0
28 June/1800 0.0
29 June/1700 2.'
30 June/lS00 3.2
1 July/1500 2.2
2 July/OOOO 1.9
2 July/1500 2.6
3 July/1500 2.5
4 July/1500 1.8
4 July/2200 1.6
S July/OOOO 1.6
S Julyl1S00 2.8
6 July/1500 0.0
7 July/1500 0.0
8 July/1500 1.6
8 July/1800 1.6
8 July/2100 1.8
9 July/OOOO 2.2
9 JulY/1600 0.0
Me" 1.45
-67-
results for all of the soundings talten at Dickinson. rather than just
one sounding per day, and that the 1982 sounding times are given in
GMT. The tabulated results are therefore not directly corrparable
with the 1981 results presented earlier.
Table 15 presents the cloud top growth llH due to seeding for
initfal updraft diameters from 1 to 6 km. The fIIilxilllJrn vertical
growth was 4.5 km; as before. the greatest vertical development due
to seeding often occurred either for the smallest or the largest
initial updraft sizes. The frequency of days with maxilllJm llH values
greater than 1 km is about the same as was found in the 1981 data.
but the frequency of lI.H values greater than 0.5 km is noticeably
less. (That may be related, in part, to the different timing of
the soundin9s used.) Because of the small number of days involved,
no frequency distributions were plotted for the 1982 data.
Table 16 sU1TIllarizes the maxil1lJm lI.H values and the tropopause
heights for each 1982 sounding. About 40S of the soundings exhibit
some potential for significant dynamic growth due to seeding (llH at
least 0.5 km) for clouds of at least some initial updraft sizes.
It is of interest to note how the dynamic seeding potential
can evolve with time during a day. For exuple. on 23 June there
was little evidence of dynamic potential in the 1500 or 1800 GMT
soundings. Indications of a substantial potential appeared in the
2100 GlT sounding, but then disappeared by 0000 GMT124 June. Similar
indications appear in the 26 June and 8 July data. Not many sets
of serial soundings are available from the NDCMP data, so this
aspect has not received IIIJch consideration in our work to date.
Table 17 presents simihr results for the increments in maximum
updraft speeds due to seeding as indicated by the GPCH model. Again,
over half the days showed some potential for increases in updraft
speed, with llW values of 2-3 m S-l being COl1'lT(ln. The values are
quite similar to those found with the 1981 Baker soundings. There
is less evidence of a systematic variation within a day than in
the lI.H values.
6.2 Comparison Q!. Model Predicted Cloud ..!Q2. Growth Due !Q. Seeding
with Observations
One~dimens;onal, steady-state numerical cloud ITlJdels like the
Great Plains Cloud Model (GPCM) have been used to assess the potential
for dynamic seeding in many places. Simpson et al. (1967) were able
to demonstrate that, for tropical comulus cloUds-:-their m:>del did a
fairly good job of predicting both the natural cloud top height and
the growth in cloud top height that would occur following seeding. An
1nvesti9ation based on similar ideas was carried out with SO!ll! of the
data from the NDCMP. This investigation is referred to here as the
-6H Stody.-
-68-
6.2.1 Background
To understand the procedure used, one rrust consider the way in
which such models are employed to assess dynamic seedabi lity. Suppose
a cloud having a given updraft size (diameter) is selected for study.
For that cloud, a IllJdel can be run in both non-seeded and seeded ver­sions
to predict the corresponding maximum cloud top heights. The
cloud itself can either be seeded or not seeded, and the resulting
cloud top height observed. (Often the maxilTltm radar echo height will
be used as a surrogate observation of the cloud top heighL) A matrix
of the possible cloud top height values can be established, using the
notat i on ill ust rated in Tabl e 18. Note that whi 1e both non-seeded and
seeded versions of the rrodel cloud are available, only one version
of the actual cloud can exist (either non-seeded or seeded).
The seedability predicted by the rrodel is, in the notation of
Table 18, (Hm~ - Hmn)' The observed response to treatment is defined
as (Has - Hmn) for seeded clouds and (Hon - Hmn) for non -seeded ones.
The appropriate one of the latter two response values can then be
compared to the predicted seedability to establish the validity of
the model in predicting dynamic seeding effects.
In the tropical cumulus investigation of Sillllson et a1. (1967),
the results were as indicated in Fig, 41, For the non:seeded clouds,
the "observed seeding effect" averaged near zero, regardless of the
predicted seedability. The fact that the scatter about zero effect
was small (except for one cloud) suggests that the mdel did a good
job of predicting the vertical development of those clouds. The
further fact that the observed seeding effect was quite close to the
predicted effect, for mst of the seeded clouds, indicates that the
seeding did have dynamic effects of the sort predicted. Moreover,
the model predicted the magnitUde of those effects rather well.
TABLE 18
Matrix of Symbols for Cloud Top Height Values
Non-seeded
Seeded
-69-
SEEDABILITY (PREDICTED)
o 1.0 2.0 3.0 4.0 5.0
5.0 ......-......,.--..,....--..---......,.--.,.
UNSEEDED
0 0 ~ 4.0 a:: D
lU
U)
~ 3.0
I-
~ 2.0
lL..
lL..
lU
1.0
(,!)
~
~O
lU
U)
SEEDED
/
/
<JY 28
/
/
/
/
/
/
/
4
~.!!.: Model-pr@dicted seedability vs. observed seeding effect for
T'lSeeded (circles) and 9 control (squares) clouds studied in 1965.
Note that the seeded clouds lie mainly along a straight line with
slope one (seeding effect 1s close to seedabi1ity). while the control
clouds lie mainly along a straight horizontal line (showing little or
no seeding effect, regardless of magnitude of seedahility). Units of
each axis in km. [From Simpson ~~, 1967]
The intent of the tiDCMP "llH Study· was to carry out a similar
analysis on North Oakota cl

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Report SOSMT/IAS/R-85/02
RESEARCH TO DEVELOP EVALUATION TECHNIQUES FOR
OPERATIONAL CONVECTIVE CLOUD MODIFICATION
PROJECTS
By: p. L. Smith, J. R. Hiller, Jr.,
A. A. Ooneaud. J. H. Hirsch,
O. L. Priegn1tz. P. E. Price,
K. J. Tyler. and H. D. Orville
Prepared for:
North Dakota Weather Modification Board
P. O. Box 1833
Bismarck, NO 58502
January 1985
Contract No. WMB-IASw80wl
Institute of Atmospheric Sciences
South Dakota School of Hines and Technology
Rapid City, South Dakota 57701-3995
Report SOSMT /IAS/R-85/02
RESEARCH TO DEVELOP EVALUATION TECHNIQUES FOR
OPERATIONAL CONVECTIVE CLOUD MODIFICATION
PROJECTS
By: P. L. Smith, J. R. Miller, Jr.,
A. A. Doneaud, J. H. Hirsch,
D. L. Priegnitz, p. E. Price,
K. J. Tyler, and H. O. Orville
Prepared for:
North Dakota Weather Modification Board
P. O. Box 1833
Bismarck, NO 58502
January 1985
Contract No. WMB-IAS-80-1
Institute of Atmospheric Sciences
South Dakota School of Mines and Technology
Rapid City, South Dakota 57701-3995
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ABSTRACT
This report summarizes some results of a broad investigation with
the objective of developing lrrproved techniques for evaluating opera­tional
convective cloud seeding projects. The research was based on
data collected in conjunction with the North Dakota Cloud Modification
Project (NDCMP) and emphas i zed the use of weather radar data and
numerical cloud m:Jdels. The research included the following
specific topics:
1) Radar echo climatology, especially as related to
potential cloud seeding opportunities.
2) Assessment of operational effectiveness in the NDCMP.
3) A variety of radar data analyses.
4) Estimation of the potential for dynamic seeding using
numerical cloud models.
5) Simulation of NDCMP seeding operations.
Some of the investigations as, for example, a study of first echo
temperatures also considered possible microphysical effects of the
seeding.
The NDCMP involves seeding for both rain enhancement and hall
suppress1on, but the research emphasized evaluation of the rain
enhancement aspect of the project. No definite evidence of rainfall
increases was obtained, but the results were generally consistent
with the seeding hypotheses and the intended effects of the seeding.
-iii-
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TABLE OF CONTENTS
~
ABSTRACT •• •••••• •••. ••••••••. •••••••••• ••• ..• ••••• •.•• •••••••••• ii i
LIST OF FIGURES................................................. vi i
LIST OF TABLES •••••••••••••••••••••••••••••••.•••••••••••••••••. xi i i
TABLE OF ACRONYMS AND SyMBOLS ••••••••••••••••••••.••••••••••••••
1. INTRODUCTION ••••••••••••••••••••••••••••••••••••••••••••••••
1.1 Historical Background ••••••••••••••••••••••••••.•••••••
1.2 Scientific Background ••••••••••••••••••••••••••••••••••
2. DATA ACQUISITION AND REDUCTION ••••••• ••• •••• 5
2.1 Radar Data Acquisition Procedures 5
2.2 Radar Data Reduction Procedures •••• •••• ••••• •••. ••••••• 10
2.3 Collection of Rawinsonde Data ••••• ••• ••••• 11
2.4 Seeding Tracks ••••••• ••••••••. ••••• •••• ••• ••••• •••••••• 15
3. RADAR ECHO CLIMATOLOGy.... ••••••••• ••••••••• 16
3.1 First Echo Development ••••••• ••• •••••• ••• 16
3.2 Distr1bution of Maxirom Echo Heights •••••••••• ••• ••• ••• 17
3.3 Distribution of Maximum Equivalent
Radar Reflectivity Factors 19
3.4 Distr1butions of Cluster Durations ••••••••••••••••••••• 20
3.5 Distributions of Area-Time Integrals
and Rain Volumes ••• 22
3.6 Distributions of Rain Amounts by
Reflectivity Factor •••••••••••••••••••••••.••••••••• 23
4. ASSESSMENT OF OPERATIONAL EFFECTIVENESS.. 24
4.1 Distributions of Seeding Events 25
4.2 Effectiveness of Hail Suppression
Seeding Operations •••••• •••••••••. •••• •••••• •••• 25
4.3 Case Studies ,. •••. 29
5. RESULTS OF RADAR DATA ANALYSES 33
5.1 Relationships Between Echo Height
and Rain Volume 33
5.2 Relationships Between the Area-Time
Integral and Rain Volume 36
5.3 Average Rainfall Rates During Storms 40
5.4 Correlations Between Echo Cluster
Characteristics ••.. •••••• ••• ••••••• ••••. ••• •••.•••• 42
5.5 Rainfall Comparisons..... 44
TABLE OF COHTENT5
(cont I nued)
5.6 Possibility of Range Bias in
the Observations •• ••• ••••••••••••••.•..•••. .••••..• 48
5.7 Development of Climatological Z~R
Relationships for Convective
Storms in the Northern
Great Plains ••••••••••••••••• ••••••••.. •••••••••••• 51
6. NUMERICAL CLOUD MODELING STUDIES •••••••••••••••••••••••••••• 58
6.1 Assessment of Dynami c Seedi ng Potenti al
with a One-Dimensional. Steady-State
Cloud Model.... •••••• •••••••.• ••••• ••••.• •••. ••••••• 58
6.1.1 Analysis of growth in cloud top
height (1981 data) ••••• ••••• •••••••• ...... ••• 58
6.1.2 Analysis of increases in updraft
speed (1981 data) ••••••••.•• •••• •••. ••• •••••• 62
6.1.3 Model analysis of 1982 Dickinson
soundings •••••••••••••••••••••••••••••••••••• 64
6.2 Comparison of Model Predicted Cloud
Top Growth Due to Seeding with
Observations •••• ••••• ••••••••••••••••• .••••••••••••• 68
6.2.1 Background .••••.••.••••••••• ••••••. ••••••••••••• 69
6.2.2 Radar data •••••••••••••••••••••••••••••••••••••• 71
6.2.3 Cloud model runs ••••• ••••.•• •••••• •••••• •••••.•• 72
6.2.4 Atte~ts to fit the llDdel to
observed cloud heights 75
6.2.5 Other observations ••••••• ••• •••..• ••••••••••• ••. 79
6.3 Sirlulation of Seeding Effects in a
Two-Dimensional, Time-Dependent.
Numerical Cloud Hodel.. ••••• •••••••. ••••••••••• ••••. 80
6.3.1 Overview of sinulation runs
and co~arison data 81
6.3.2 Sinulation of 22 June 1982 case •• ••••• 82
6.3.3 Attempts to s;rrulate the
8 July 1982 case •••••••• ••••• ••••••. 87
7. CONCLUDING REMARKS.......................................... 88
ACKNOWLEDGMENTS ••••••••••••••••••••••••••••••••••••••••••••••••• 89
REFERENCES ••••• ••••• •••••••• ••••••• ••••• ••••• ••••••• •••• •••••••• 90
LIST OF FIGURES
Map show1 ng the locat ions and 150-km range
coverages of the fIDCMP District I (Bowman)
and District II (Parshall) radars •••••••.•••••••••••••
Schematic of operational and research areas
and equipment allocation for the 1982 field
study ••••••••••••••••••••••••••••••••••••••••••••••.••
Plot of the number of days for which radar
data were recorded at Dickinson by time
of day ••••••••••••••••••••••••••••••••••••••.•••••••••
Schematic diagram of the data reduction
procedure used for the NOCMP weather
radar data tapes ••••••••••• ••••••••. ••••••. ••• ••• ••••. 10
Example of a low-ti It PPI map for the
District I (Bowman) radar at 23:01 GMT
on 15 August 1981 •••• •••••••••••••• •••. •••••• •••••...• 12
Frequency distribution of 155 soundings
taken in 1981 at Baker, MT, vs. time
of day...... ••••• •••. •••••••••• ••••••••• •••• •••••••••. 14
Frequency distribution of 31 soundings
taken at Dickinson, NO, in 1982 vs.
time of day.. •••••••••••••• ••••••••• •••••••• ••••• •••.• 14
Composite frequency distribution of the
times of ori91n of the cluster echoes,
for the 1981 80wman radar data •••.••••••••••••••••.••• 17
Percentage frequency distribution of
333 first-echoes vs. temperature •• ••• ••• ••• •••• ••••..• 18
10 Percentage frequency distribution of
MEH values for 351 Dickinson (1982)
echo clusters •••••••• •••• ••••• •••• ••••• •••. •••• ••• •••• 18
11 Cumulative frequency (%) distribution of
maximum equivalent radar reflectivity
factor values ••• ••••••• •••• ••••• ••••••• •••. ••••• ••• ••. 20
12 Cumulative frequency distributions of
radar echo cluster durations •••••• ••• •••• ••• ••• 21
13 Cumul at i ve frequency di stri but; ons of
1982 Oickinson cluster durations ••••• •••••• ....... •... 21
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LIST OF FIGURES
(cor1tinued)
Number .!.i..!..!.£. ~
14 Relative frequency distributions of the
25-dBz ATI and RERV for echo clusters
observed in July 1981, Bowman, NO •••••..• •••. •••••• ••• 22
15 Cumulative frequency distributions (%)
of rainfall volume vs. equivalent radar
reflectivity factor (Ze) for 1981 Bowman
and 1982 Dickinson data •••• 23
16 Spatial distribution of 1981 seeding
events for NDCMP District I •••••• •••••• ••• ••••• 26
17 Distribution of 19BO NDCMP seeding events
for District I, by time of day........................ 27
18 Distribution of 1980 NDCMP seeding events
for District Il, by time of day....................... 27
19 Histogram showing the distribution of
timeliness of initial seeding events
for hail suppression using a 45-dBz
hai 1 threat threshold '0' ••••• 0" 28
20 Same as fig. 19, but for a 50-dBz
threshold •• ••••••••• •••• •••••• ••••• ••••. .•••••••••• ••• 28
21 Same as Fig. 19, but using 11 45-dBz
reflectivity threshold and a MEH
threshold of 35,000 ft (10.7 km) 30
22 Same as Fig. 19, but usin9 a 50-dBz
reflectivity threshold and a MEH
threshold of 35,000 ft (10.7 km) 30
23 Scatter plot showing the relationship
between maximum radar echo heights and
radar estimated rainfall volumes for
the 1982 clusters ••••• •••••••.•• ••• ••••.•.•.. •••••.••• 34
24 Scatter plot and regression line of ec.ho
cluster rain volumes vs. 25-dBz area-time
integrals for 1981 Bowman data 3B
25 Scatter plot comparing RERV from echo
clusters with thei r ATI •••••• ••••••• 39
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LIST OF FIGURES
(continued)
Humber Title ~
26 Cof'l1)arison of echo cluster ratn volumes
cOlllluted from Z-R conversion and inte­gratton
with corresponding volumes
estimated from the 1980 rain volume
vs. 25-dBz ATI formul a •••••••••••••••••••••••••••••••• 40
27 The relative frequency distributions
of cluster average rainfall rate for
growing and decayin9 periods 42
28 Comparison of frequency distributions
of the RERV for seeded and non~seeded
echo clusters for the 1981 80wman data 47
29 Comparison of frequency distributions of
the RERV for in-district and out-of­district
echo clusters; 1981 District I
(Bowman) data ••••••••••••••• ••••••••• •••• ••••• ••• ••••• 47
30 Comparison of frequency distributtons of
the maximum reflect1vity factors for
nearby and Il'()re distant echo clusters;
1981 80wman data.. •••••• •••••••• •••••••• ••• ••• 49
31 Two-dtmensional frequency distributton of
maxillUm equivalent radar reflectivity
factors for 851 echo clusters as a func­tton
of range from the radar (1981
80wman and 1982 Of cl:. t nson data) 49
32 Comparison of cUlOOlathe frequency
distributions of reflectivity factor
values based on areal coverage for
in-district and out"'Of-dtstrict
clusters; 1981 Bowman data 50
33 Comparison of cumulative frequency
d1 str1 but ions of reflect i vity factor
values based on areal coverage for
In-district and out-of-district
clusters; 1982 Oick-inson data.......... ........ ....... 51
34 Graph showing observed cluster HEH as
a function of range from the radar 52
LIST OF FIGURES
(continued)
35 Cumulative frequency distribution of
5-cm equivalent radar reflectivity
factors> 42 dBz obtained during
1982 in southwestern NO •••••••••••••. ...... •••••• ••••. 53
36 Map showing the locations of the Dickinson
radar site and the Newell Agriculture
Research Station's watershed studies •••••••••••••••••. 54
37 Curnul at i ve frequency di stri bution of 5-mi n
rainfall rates observed over the period
1957-1970. using data from the Newell ARS
site ••••.• 55
38 Frequency distribution of maximum model
predicted cloud top growth expected due
to seeding usin9 1300 MDT (1900 GMT)
1981 Baker sounding data as input •.•. 60
39 Frequency di stri but ion of rrode1 predi cted
lIH values due to seeding for 5-km diameter
updrafts; 1300 MDT Baker 1981 soundings
used as input 60
40 Frequency distribution of cloud model
predicted increases in maximum updraft
speed due to seeding; 1300 MDT Baker
1981 soundings used as input 62
41 Model-predicted seedability vs. observed
seeding effect for 14 seeded and 9 control
clouds stUdied in 1965 .. 70
42 Observed seeding effect vs. predicted
seeding effect for the initial "ll.H"
calculations •••••••• .... ••••••••••• •••••. ••• ••••• ••••• 75
43 As in Fig. 42, except that the rrodel
cloud heights were found by running
the model in an inverse rrode .. 78
44 Observed cell radar echo radius vs. the
inferred roodel cloud updraft radius.. 79
LIST OF FIGURES
(continued)
Number Title ~
45 Plots of the maximum mixing ratio
of graupel in the sill1Jlated clouds
as a function of time 85
46 Plots of the maximum mixing ratio
of snow in the simulated clouds
\IS. time ••••••• •••••••••••••••••••••••• •••••••• •••. ••• 85
47 Comparisons of accumulated rain at
the ground at 75 min roodel time 86
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LIST OF TABLES
Radar System Specifications for
EEC WR-I00 ••••••••••••••••••••••••••••••••••••••••••••
Summary of Radar Data Collected .
Reflect; vity Factor Code Used for
Low-Tilt PPI Printouts •••••••• ••••••••••••••• •••• ••••• 13
COllllari son of Fi rst-Echo Data from 1982
Dickinson. NO, Observations With Those
1n Koscielski and Dennis (1976) •••••••• •••••• ••••••••• 19
Regression Parameters for Cluster Echo
Height - Rain Volume Relationships
(1982 Dickinson Radar Data) ••••• ••••••• •••••. 35
Average Values of Some Echo Cluster
Characteristics ••••••••••••••••••••••• ••••• ••• •••. •••• 43
Correlation Coefficients Between Echo
Cluster Characteristics for the 1982
R.adar Data •••••••• ••••••••••• •••• •••• ••••• •••••••••••• 43
Comparison of Average Values of Some
Characteristics of Seeded and Non-
Seeded Echo Clusters (1982 Data) 45
Comparison of Correlation Coefficients
8etween Echo Cluster Characteristics
for Seeded and Non-Seeded Clusters
(1982 Data) •••••••••••• ••• •••• •••••• ••••••••••• ••••••. 46
10 Rainfall Rates Calculated from Different
Ze-R Relationships ••••••••.•• •••• •••••••••••. ••••• •••• 56
11 Model-Computed llH Values (km); 1300 MDT
(1900 GMT). Baker, MT, 1981 Sounding nata ••••• 59
12 Summary of llH Values; 1981 Baker, MT,
1300 MOT (1900 GMT) Sounding Data ••...• 61
13 Model-Computed bW Values (km); 1300 MDT
(1900 GMT), Baker. MT, 1981 Sounding Data ••••••••••.•• 63
14 Summary of liW Values; 1981 Baker, MT.
1300 MDT (1900 GMT) Sounding Data •• 64
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15
L1ST OF TABLES
Title
!"()del-COll1>uted AH Values - 1982
Dickinson Soundings •.••••.••••••••..••.• 65
16 Sunmary of Maxil11Jm AH Values - 1982
Dickinson Soundings •.••••• •••• ••• ••• ••••••• ••••••.•••• 66
17 Increase 1n Maximum Updraft Speed
(1111) DJe to Seeding 1982 Dickinson
Soundings; S-k.m Updraft Diameter 67
18 Matrix of Symbols for Cloud Top
Height Values ••••••••••••• •••••• ••. •••••• ••• •••••••••• 69
19 Results of l1H Study for Seeded
Cells ••• ••••••• ••••••••••••••••.••.. ••••• •••.••••.••.• 73
20 Results of tl.H Study for Non-Seeded
Cells ••••••••••••••• .••• •••••• •••••.•• ••••••••••••••.• 74
21 Results of Inverse-Hode AH Study
for Seeded Ce 11 s ••••.•••••••••••••.•••••••••••••••...• 76
22 Results of Inverse-Hode AH Study
for Non-Seeded Cells ...••••••••••••••••••••••••••••.•• 77
23 Total Precipitation •••••••••• •••••• ••••••••••••••••••• 86
-xiv-
AT!
BAK
BIS
BOl~ or BOO
°c
CCOPE
COT
OME
CSU
OIK
DVIP
GMT
GGW
GPCM
lAS
LOT
MDT
MEH
MLR
MLS
MST
NCAR
NOeM?
NOPP
TABLE OF ACRONYMS AND SYMBOLS
Area time integral
Baker, MT
Bismarck, North Dakota
Bowman, North Dakota
Degrees Celsius
Cooperative Convective Precipitation Experiment
Ceotral Daylight Time
Distance Measurinlj Equipment
Colorado State University
Dickinson, North Dakota
Digital Video Integrator and Processor
Greenwich Mean Time
Glasgow, Montana
Great Plains Cloud Model
Institute of Atmospheric Sciences
Local Daylight Time
Mountain Daylight Time
Maximum echo he; ght
Multiple linear regression
Miles City, Montana
Mountain Standard Time
Nat i Dna 1 Center for Atmospheri c Research
North Dakota Cloud Modification Project
North Dakota Pi lot Project
TABLE Of ACRONYMS AND SYMBOLS
(continued)
NOWHO North Dakota Weather Modi fication Board
NOAA National Oceanic and Atmospheric Administration
PAR Parsha 11, North Dakota
PPI Plan Position Indicator (radar screen)
PRF Pulse Repetition Frequency
RERV Radar estimated rain volume
SDSM&T South Dakota School of Mines and Technology
UNO Uni versity of North Dakota
VDR VHF Omni Range
WMI Weather Modification. Inc.
ZMX Maximum equivalent radar reflectivity factor (dBz)
toH Hodel predicted cloud height change due to seeding
boW Hodel predicted change in maxill1Jm updraft
velocity (w) due to seeding
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1. INTRODUCTION
This report summari zes research conducted under the referenced
contract from May 1980 through July 1984. The research was carried
out as part of a federal/state cooperative program to develop il1llroved
eval uat i on techni ques for operat iona 1 weather nodi fi cati on projects.
The agency responsible for the federal side of the overall program is
the Nat i ana 1 Oceani c and Atmospheri c Adm; nistrat i on (NOAA). The North
Dakota Weather Modification Board (NDWMB) is the state agency that
coordinates this part of the program. which is directed towards the
eva1uat i on of projects deal i ng with sunmer convect i ye clouds.
The North Dakota Cloud Modifi cat i on Project (NOCMPj. conducted
for both precipitation enhancement and hai 1 suppression by seeding
such clouds with ice nucleating agents. was selected as a "test bed"
for this study. The contract under which this report was prepared
is one of several coordinated by the NOWMB in an effort to develop
improved physical and statistical techniques for evaluating such
projects. Application of the resulting techniques to operational
projects such as the NDCMP should lead to a better understanding of
the effects of ice phase seeding on conyecti ye clouds.
The South Dakota School of Mines and Technology research
concentrated on those aspects of the evaluation problem that can
be examined primarily by using quantitative weather radar data
and numerical cloud fT(ldels. The research under the contract han
several mai n objecti yes:
1) To develop climatological information to ain in
defining the potential for cloud seeding opportunities
in the NDCMP;
2) To investigate the potential for dynamic seeding in
North Dakota through data analysis and the application
of numerical cloud models to the NDCMP region;
3) To assess the effectiveness of the NOCMP operations;
aod
4) To develop evaluation techniques for operational
weather fT(ldification projects based on the use of
di gital weather radar data.
1.1 Historical Background
The evaluation of operational weather modification projects has
yielded a substantial amount of useful scientific information in the
past. Changnon et al. (1979) recently stresseo again the importance
of an evaluationcapability for such projects. Smith et al. (1973)
conducted some eva luat i on studi es of South Dakota 's operatT'Ona 1
-1-
weather nodification project, which was similar to the current
NDCMP, using radar and sounding data. However. IIDst of the research
related to evaluating operational projects has involved statistical
approaches. For example, the Illin01s State Water Survey has been
developing statistical techniques for evaluating operational weather
modification projects (Hsu et al., 1981; Hsu and Changnon. 1984).
Gabriel and Petrondas (1983/emined some of the statistical
procedures cOlTHOOnly used for evaluating operational projects.
The present federa 1Istate cooperat 1',Ie program was developed in
response to recommendations made by the Weather Modification Advisory
Board (l978). The responsibility for carrying out the federal side of
this program to evaluate ongoing weather rrodification projects, both
summer and winter, was assigned to NOAA. Colorado State University
(CSU) developed a general design for the program under a NOAA contract,
and the design of the investigations in North Dakota has been based on
recolllllendations deri ',led from the CSU work. The work in North Dakota
was conducted through a cooperat i ',Ie agreement between NOAA and the
NDWMB.
Field work began in North Dakota in tile sunmer of 1980 with tile
collection of digital weather radar data at two of the NOCHP district
radar sites (Bowman and Parshall). A previous report (Brown et a1.,
1980) describes the collection of the data in 1980 and Smith et'iT":'"
(1981) discuss the pre11minary analysis of those data. For the SUiilmer
of 19B1, the investigation focused on trying to establish the validity
of the dynamic seeding approach in North Dakota. The measurement
capability was expanded to include an aircraft equipped with basic
cloud physics instrumentation; that a1rcraft was operated by the
University of North Dakota (UNO). Miller et a1. (1981) SUlll'Rclrize
the radar data collected from the same sites-a5"""in 1980. Another
report (Smith & lli. 1982) sultll1ari zes the pre1imi nary ana lyses of
the 1981 data and also provides a rore complete description of the
1980 data analysis.
For the sult'lTer of 1982, additional 1~roveraents were made to the
project design and to the facilities. The scientific e~hasis was
broadened to include investigation of microphysical as well as dynamic
effects of seeding. A radar and raw1nsonde system dedicated to tile
research function were operated at Dickinson. The UNO operated a
cloud physics aircraft with improved performance and instrumentation.
Smith et & (1983) discuss the data collection and preliminary
analysTS work for the 1982 season. Since that time the effort has
been devoted to data analysis, and the present report contalns the
main results of all of the analyses carr1ed out under the contract.
Partial reports of some of these results can also he found in Miller
!!..!h (1983). Ooneaud!!.!b. (1984a.b), and Smith!!..!h (1984a,b).
1.2 ScientHic Background
The North Dakota Cloud ModHication Project, like any other weather
modifi cat ion project, involves three primary aspects:
-2-
1) Identification of seeding opportunities.
2) Treatment of the clouds.
3) Evaluation of the results of treatment.
The operational procedures for the NOCMP are spelled out in a
detailed Operations Manual (NDWMB, 1980). The research discussed
herein emphasizes evaluation of the effects of the seeding, although
the identification and treatment aspects of the project were also
explored.
The operational cloud seeding in the NDCMP is conducted using
glaciogenic nuclei in an atterrpt to freeze supercooled cloud liquid
water and/or raindrops. The objectives include both precipitation
enhancement and hail suppression. Regardless of the details of the
mechanism by which precipitation is produced, such ice phase seeding
can, and probably does, influence both the microphysical and dynamic
characteristics of the clouds. The principal microphysical effect is
to accelerate the development of precipitation, or to cause it to occur
at lower altitudes in the seeded clouds. This microphysical change,
however. rust inevftably feed back into the dynamic behavior of the
clouds. That can occur in at least two ways:
1) The freezing of the supercooled liquid releases latent
heat of fusion which, under the appropriate circum­stances,
increases the buoyancy and the associated
updraft speed; and
2) Increased precipitation loading due to the earlier or
lower development of precipitation may modify the up­and
downdrafts; the associated outflow in the houndary
layer may, in some circumstances, lead to secondary
growth of clouds neighboring the ones seeded.
These effects have been explored in a numerical cloud IIDdel by Orville
and Chen (1982).
In some projects, glaciogenic seeding is conducted primarily to
affect the dynamic behavior of the clouds. Elementary indications of
dynami c effects of seedi ng are avai 1ab1e from one-di mens i ana 1, steady­state,
numerical cloud models such as the Great Plains Cloud Model
(Hi rsch, 1971). The effects of seeding on vertical cloud growth as
indicated by such roodels have been confirmed for tropical clouds
(Simpson et a1., 1967), but direct confirmation for clouds of the
northern "'H'igDlains is lacking. Such IIDdels are also not very
satisfactory for estimating the amount of precipitation that results.
Results of the North Dakota Pilot Project (Dennis et al •• 1975a)
showed evidence of rainfall increases on days for Wh";'ctlthis type of
cloud IIDdel indicated appreciable dynamic growth due to the seeding.
Perhaps the nost detailed hypothesis invoking the dynamic seeding
-3-
approach is that described by Simpson (1980). Dennis and Schock
(1971) found evidence of dynamic effects of seeding in experiments
carried out in the Dakotas.
The NDCMP Operations Manual ;s not very specific about the nature
of the dynamic effects expected. In fact, the CSU investi9ators raised
quest Ions about whether the seedi ng rates used are adequate to cause
significant dynamic effects in the clouds that are seeded primarily for
rainfall enhancement. However. they did suggest that in those NDCHP
-Type 8- clouds that are seeded for hal1 suppression. the seeding
rates might be adequate to produce dynamic effects that could enhance
the rainfall. These Type B clouds are rou9hly equivalent to the
"convect; ve compl exes II ; dent; f; ed in Montana stud; es by He; mbach and
Super (1980) or the "small mesoscale areas" of Austin and Houze (1972).
ThUS, one goal of this research was to clarify the applicability of
the dynamic seeding concept in the North Dakota clouds.
-4-
2. DATA ACQUISITION AND REDUCTION
2.1 Radar Data Acquisition Procedures
The radar systems used in support of this investigation were
Enterprise Electronics Corporation WRIOO-2 systems with basic charac­teristics
as summarized in Table 1. These small C-band radars have
been used for directing the cloud seeding operations in the NOCMP. In
fact, for the first two years of field work under this project the data
were actually recorded from operations control radar sets. That pro­duced
some conflicts between the requirements for directing the seeding
operations and collecting data for the research. so for the third field
season, a separate radar was dedicated to the research function.
The radar sites for the first two surrmers (1980 and 1981) were at
Bowman and Parshall (Fig. 1) field headquarters for NDCMP Districts I
and II, respectively. For the third year (1982). a single research
radar was located at Dickinson (Fig. 2), in between the two NDCMP target
districts. The move was intended to improve the data for the target
districts in two ways. One was that the life histories of radar echo
clusters in the districts would no longer be interrupted by the absence
of data from a 20-km radius "donut hole" around the radar site. Echoes
from withi n thi s 20 km ci rcle were not recorded because of ground
clutter contamination and the requirement to go to excessively hi9h
elevation angles. The second improvement came about because the radar
scan cycle could be speeded up as use of the radar no 10nger had to he
shared with the NOCMP fi e1d rreteorol ogi sts.
Generally. data were recorded whenever there was significant
convective activity in the region within, roughly, a 150-km range from
a radar site. This included times when the NDCMP cloud seeding opera­tions
were in progress. as well as frequent occasions when no seeding
was being carried out. The recorded data include flI1Cf1 weather acti­vity
outside the NDCMP districts to provide a sample of unseeded
cases for comparisons with cases in the districts themselves. Data
from several occasions involving stratiform precipitation were also
recorded. but there has not been l1lJch detailed analysis of those data.
The radars were operated in a volume scan mode. with two low level
360° azimuth scans at P elevation angle and succeeding scans at 1°
elevation increments. The elevation steps used were smaller than the
2° elevation beaiTh'lidth of the radar antenna because better resolution
for the determination of echo heights was desired. For 1980 and 1981.
the scans were continued to an elevation of 15°, or until no echoes
were observed on an azimuth scan (if a lower elevation angle took the
antenna beam above the cloud tops). This scan cycle required about
6 min to complete, with the antenna rotating at about 22 sec per revo­lution.
The objective was to complete one scan every 10 min; the
remaining time was used by the NDCMP radar meteorologists to collect
observat ions needed for di recti ng the seedi ng ope rat ions. Thi soften
-5-
TABLE I
Radar SystcllI Specifications for Efe \'tR-IOO
Transmitter
Wavelength, ),
Pulse duration, 'l:
Pulse repetition frequency, F
Peak power output, Pt
Antenna
Refl,ector size
Antenna pattern
Antenna gain
AziJm.lth readout
Elevation
Elevation readout
Polarization
Receiver
folini'lium detectabl e
signal. Smin
Operating frequency, f t
Response characteristic
5.4 cm
2J,Jsec
256 s-l
200 kW nominal
1.S3 m diameter
2.1 0 conical beam
37 dB nominal
Digital to 10.10
-2 to "'600
Digital to 10.1 0
Horizontal
From calibration
5600 MHz
Logarithmic
!signal Processing
Digital Video Integrator and Processor (DYIP)
-6-
proved insufficient when many echoes were in the area so. in practice,
the scan interval sometimes increased to 12 min or roore. This con­flict
between requirements for using the radar to acquire research
data and observations for conducting seeding operations led to the
decision 1n 1982 to provide a separate radar at Oickinson for the
research program. The max11JlJ1Il elevation angle was then reduced to
12°, and the scans were routinely conducted on a 6-min cycle.
The radar systems were calibrated according to customary
procedures. The data system and the calibration procedures were
upgraded each year on the basis of previous field experience, so that
continual refinements took place. A cOfllllete receiver/transmitter
calibration was usually carrfed out twice per week to obtain values
for the transmitted frequency, power, and pulse repetition frequency.
The pulse shape and duration were checked occasionally, and the
antenna gain was measured once each season. The antenna orientation
was first Checked by solar rethods in 1982. so there may be some
errors in the elevation angle data from the previous seasons.
Receiver calibration data were usually recorded on the tapes before
and also after each data recording session. This led to values for
slope and intercept parameters for a linear fit to the receiver
calibration curve for each set of recorded data.
-7-
Montanill
Wyoming
North Dakota
f:i..!L..~: Schematic of operational and research areas and equipment
iTTOcation for the 1982 field study.
The data recording format and the data handl i ng procedures were
simi Jar to those used in HIPlEX (see Schroeder and Klazura. 1978. for
details). Data were recorded at 1° azimuth 1ncrements with l-km range
bins along the radar beam. The video integration employecl 16 pulses at
a radar PRF of about 256 pulses per second. During each scan, an azi­IIIlth
sector of about 20° or larger was omitted froo the recorded data
to permit the antenna to step up to the next elevation angle whi Ie
passing through this Mblank sector." The sector location was adjusted
by data collection personnel to minimize loss of significant data.
The first 20 km of range clata were electr0f'l1cally discarded to elimi­nate
the majority of the ground clutter from the recorded data. but
considerable clutter still affected some of the data at the lower
elevation angles.
Table 2 sUmll\ar1zes the radar data recorded during this project at
the different sites. The variations are due to a combination of flEteo­rolog1cal
factors (e.g •• 1980 was very dry in western North Dakota),
varying length of the research field seasons (the 1982 season was only
one mnth in duration). equipment difficulties (especially 1n early
1980). dnd other factors. Figure 3 shows thl~ variation of the
-8-
TABLE 2
Summary of Radar Data Collected
Data Collection No. Days No. Hrs. No. of
Year Radar Site Period With Data of Data Data Tapes
1980 Bowman 17 Jul-31 Aug 19 72 25
1980 Parshall 28 Jul-28 Aug 18 51 18
1981 Bowman 1 Jun-23 Aug 51 185 96
19B1 Parshall 1 Jun~20 Aug 46 168 78
19B2 D1ckinson 6 Jun- 9Jul 21 87 43
15r----T-'=-==;r.::.:..:....=="T------,
~
J:
f-
~ 10
(f)
>­<
1. o
u.
o 5
a:
w
eratures found using 1982 Dickinson data. The median first-echo
te""erature was _8.2°C.
Table 4 displays the main first-echo results and COl1l'ares these
obser....ations wah those reported by Koscielski and Dennis (1976).
Because fewer than 5~ of the 1982 cases were seeded clouds, the values
shown from Koscielski and Dennis are for no-seed days. The means and
standard deviations are quite similar, and the ranges are also com­parable.
The 1982 mean (1 rst-echo tel1l'erature of _9°C suggests that
the main predpitation-forming Ilechanism is probably an ice process.
However. a sizable fraction of cases (more than one-third in 1982)
have first-echo temperatures higher than -SoC. As noted by
Koscielski and Dennis. this seems to suggest the likely ilJllortance
of coalescence processes in at least some of the clouds. However.
particle recirculation processes may also contribute to the
occurrence of "high-temperature" first echoes.
3.2 Distribution Q!. MaxillJJm ~ Heights
A maxillJJm echo height (MEH) was determined for each cluster on
each radar scan. Then for the total cluster lifetime. the overall
lIIaxilll.lm was determined. Figure 10 shows the frequency distribution
of those cluster MEH values from the 1982 Dickinson radar data. They
range from about 2 to 20 km. with a median value of about 8 km. This
is rather simi lar to the distribution reported hy Smith et a1. (1973)
for maxilllJm echo heights in South Dakota, although the latter-was
compiled on a scan-by-scan rdther than cluster-by-cluster basi'S.
-11-
301,---------------------,
~ -40 -~ -~ -~ -20 -15 -10 ~
TEIlPERATURE OF FIRST ECHOES Ie}
10 15
~~: Percentage frequency distribution of 333 first-echoes vs.
temperature. The average fi rst echo temperature (FEl) was _9.3°C
with a standard deviation of 9.4Co.
18 20
~.!.Q.: Percentage frequency distribution of maximum echo height
(MEH) values for 351 Dickinson (1982) echo clusters. The average
MEH was 8.6 km.
~18-
TABLE 4
Comparison of First-Echo Data from 1982 Dickinson,
North Dakota Observations With Those in
Koscielski and Dennis (1976)
Sample size
Fi rst-Echo Height:
Mean (MSL)
Standard Deviation
Fi rst-Echo Temperature:
Mean
Standard Deviation
Extremes
Percent> _5°C
Percent> DOC
333
5.0 km
1.5 km
_9.3°C
9.4Co
+12, _43°C
38
10
Koscielski
and Dennis
~-seedDaYs)
133
_1O.8°C
9.4Co
+5, -29°C
25
7.5
The distribution in Fig. 10 differs noticeably from one presented
earlier (Smith et al •• 1983) for the 1981 Bowman radar data. There
may be two explana'ffOns for the difference: First, the weather during
the 1982 data collection period included a number of relatively cold
stratiform rainfall situations which might shift the distribution
towards lower maximum echo heights. Second, the radar antenna eleva­tion
angles were checked by solar methods in 1982. whereas that was
not done in 1981. Consequently. there is reason to suspect possible
elevation angle errors in the 1981 data. The error in 1981 is esti­mated
to be of the order of 0.5° (recorded va lues too hi gh) based on
the need to correct the antenna alignment by this amount as a result
of the initial 1982 solar measurements.
3.3 Distribution.Q.!. Maximum Equivalent Radar Reflectivity Factors
A similar analysis was made of the cluster maximum reflectivity
factors. The cUlTkllative frequency distribution shown in Fig. 11 was
constructed by combining maximum equivalent radar reflectivity factor
values for 566 echo clusters observed from Bowman (1981) and 302
observed from Dickinson (l982). The values range from less than 2':)
to Ill)re than 65 dBz. and the median cluster maximum reflectivity
factor is about 43 dBz.
-19-
100
80
70
10
oL~_......,=~_,-~_--,--_~.-J_~_--'--_~.-J
15 3 ~ 45 55 65 75
M~XlMUM EiWIV",LENT RAOt.R REFLECTIVITY FA.elDA (dSz)
.E.!..5c..l!.: Cumulative frequency (%) distribution of maximum equivalent
radar reflectivity factor values; 556 echo clusters from Bowman (1981)
and 302 from Dickinson (1982) were combined to generate this graph.
The diagram reveals that 27% of all the recorded clusters exceeded
50 dBz sometime during their life histories. This suggests that about
one storm out of four in North Oaktoa reaches hail threat status
(assuming that 50 dBz is a reasonable 5-cm radar threshold value for
a hail threat). Strong hail potential (Ie). 55 dBz) is indicated in
about 12% of the echo clusters observed. while only 3% exceed 60 dBz.
This supports the idea that the rare events account for roost of the
devastatin9 hail losses.
3.4 Distributions Q.f.. Cluster Durations
Frequency distributions of the echo cluster durations were prepared
separately for each year. For both the 1980 and 1981 data, the median
duration was a little over one hour (Fig. 12). The longest cluster
duration in 1980 was a little over four hours, while the maximum dura­tions
for the wet 1981 summer were ~;omewhat longer. The distributions
are approximately log normal, except for a deficiency of long-lived
clusters. That is probably due to echoes moving out of radar range
before their full lifetimes could be determined. The way echo mergers
were handled in the data reduction (cL Sec. 2.2) may affect the
detailed form of these distributions somewhat.
The distributions for 1982 were similar to those of earlier years
(Fig. 13). except that more short-lived clusters were identified
because of the shorter radar scan cycle. That reduced the median
duration to about 0.8 hour in the 1982 data.
-20-
::,---------------------."
6.0
5.0 • JULY·AUGUST 1980 DATA
F 4.0 0 JUNE·AUGUST 198t DATA
"-
z 3.0
0i 2.0
~ 1.0
O.51.~2:_75-t1O~:;;;20:--7.50:--'.;-0 --;:,,;-;;,;:-,--c,io,----."".•",:O-':,.,
CUMULATIVE FREQUENCY (%)
~ 12: Cumulative frequency distributions of radar echo cluster
duratTOns; 163 Bowman and Parshall (l980) clusters and 587 Bowman
(1981) clusters were used to generate the respective lines.
DICKINSON 1982
x All clusters
Fullilletlmes
Q2 1 ~ W M
CUMULATIVE FREQUENCY (%)
~.!l: Cumulative frequency distributions of 1982 Dickinson cluster
durations. The "x" 's denote all clusters (351) and the dots denote
only that subset of clusters (122) whose full lifetimes were observed
by the radar.
-21-
Questions arose about the possible effects on these distributions
of echo duration due to clusters rooving out of radar range and there­fore
being assigned too short durations. As noted above. that may be
responsible for the deficiency of long-lived clusters indicated in
Fig. 12. A separate distribution was therefore prepared from the 1982
data including only clusters that passed through their complete life­times
within radar range. Figure 13 also shows that distribution;
the overall effect is to shift the whole distribution toward shorter
durations, because this restriction removes many long-lived clusters
from the sample entirely. However. the general indication of typical
cluster lifetimes is not greatly different. Consequently. the evi­dence
about this effect on the distribution remains somewhat mixed.
3.5 Distributions of Area-Time Inteqrals and Rain Volumes
Doneaud et a1. (1984b) show that the Area-Time Integral (ATI) is
closely connecte 37 dBz. Using le-R relationships
applicable in North Dakota, this "median le" can be translated to a
"median rainfall rate" of about 6 IlI11 hr- 1• It is interesting to note
that little of the total rain {< 10%.} falls at reflectivity factors
> 50 dBz. This is the part of the storms, however, tnat produces
most of the damaging hail.
The differences between the 1981 and 1982 data in Fig. 15 are
small, indicating consistent performance of the radar system. The
19B2 field season was briefer and included fI'Ore early~summer strati­form
ra; nfa11, so the sense of the di fference in the fi gure is
plausible.
w~
!il 80
z
~
~
0.w, 60
~
ffi
" 40
~
w
>
~ 20
~"
0 60 70 10
Ze' dBz
~~: Currulative frequency distributions (%) of rainfall volume
vs. equivalent radar reflectivity factor (Ze) for 1981 Bowman and
1982 Dickinson data.
-23-
4. ASSESSMENT OF OPERATlOHAl EFFECT! VENESS
Another objective of this research was to assess the effectiveness
of the NDCMP operations and make recommendations for improvement. This
part of the investigation sought answers to questions such as:
1) How well did the NOCMP operators succeed in recognizing
all of the seeding opportunities?
2) Were they able to treat all the opportunities?
3) Was the treatment conducted in the right place and at
the right time?
Questions concerning the types, amounts, I1'Odes of delivery, Mel
effectiveness of the seeding agents employed were not considered
in this study.
This operational assessment was to be carried out after the fact,
and largely with reference to the recorded radar data, a circumstance
which would provide the advantages of hindsight and ample time for
evaluation of the data. However, a serious limitation to the use of
the radar data for identifying seeding opportunities arises because
there is no di rect correspondence between radar echoes and seedi ng
opportunities for precipitation enhancement. Kat every seeding oppor­tunity
is associated with a radar echO; some clouds that never produce
echoes should be seeded to stilftllate the development of precipitation.
At the same time, not every echo represents a seeding opportunity;
some echoes correspond to clouds that are not suitable for seeding for
a variety of reasons. The precipitation may be well developed before
the clouds come within working range of the target districts; the
observed inflows to the clouds may be weak; or the clouds lJIay be
expected to develop so efficiently through natural processes tl'1at the
NOCMP operators elect not to seed them. Sometimes new clouds that
develop in the vicinity of neighboring echoes are not regarded as
suitable candidates for seeding because of expected dynamic or
carryover interactions.
Moreover, radar data were generally collected in this project only
when seeding operations were in progress or regarded as imminent,
because the radar was not manned on a continuous basis. Opportunities
may have occurred outside the periods when the radar data were being
recorded; if so, they would not be reflected in the data set. Thus
the radar data alone are not sufficient to identify the seeding oppor­tunities
for rain stilTRJlation. Consequently. this approach to the
assessment of operationa 1 effect i veness can produce only liPli ted
results regarding the rain enhancement aspect of the NOCMP and was
therefore not carried very far. The situation concerning the iden­tification
of opportunities for hail suppression is somewhat better,
and Section 4.2 discusses some investigations of that aspect of the
NOCMP.
4.1 Distributions of Seeding Events
Information about seeding opportunities may be difficult to
extract from the radar data, but information about seeding events is
obtai nabl e from the records of the NOCMP seedi ng operati ons:-Th'e
seeding events provide some indication of the frequency and location
(in space and time) of seeding opportunities. although for various
reasons there tend to be many more opportunities than events.
The spatial distributions of the reported seeding events for 1980
and 1981 were examined (Smith et al •• 1981, 1982). In the first year,
there seemed to be relatively llttTe seeding activity toward the
upwind (westerly) edges of the districts and perhaps too flJJch activity
near or beyond the downwind (easterly) edges. That may have been
related to the very dry conditions encountered in 1980, particularly
in eastern Montana and western North Dakota. Nevertheless. the
information was passed on to the NOCHP operators. Interestingly, in
1981 more seeding activity occurred along and beyond the upwind edges
of the districts (see, for example, Fig. 16).
The distributions of seeding events in 1980 by time of day were
also examined (Figs. 17, 18). They generally resemble the distribu­tion
of the beginning times of echo clusters presented in Fig. 8.
The resurgence in act i vi ty around sunset is especi a11y evi dent in
Figs. 17 and 18 (note that these figures are plotted in local time).
In comparing these figures, one should note that the numbers of
seeding events will not correspond in any simple way to the number
of radar echo clusters, because 11 cluster seeded for hail suppression
may be associated with several events.
4.2 Effectiveness of .!:!!!.l Suppression Seeding Operations
Data from 28 days using 1981 Bowman radar observations and 11 days
using 1982 Dickinson radar observations were combined to study the
NDCMP hail suppression seeding response times. First, 180 echo
clusters with maximum equivalent radar reflectivity factors). 45 d8z
were identified in the NOCMP target areas. This vallIe is somewhat
above the NOCHP Operations Manual hail suppression seeding threshold
of 40 dBz and should indicate those clusters posing a realistic hai 1
threat. Seventy-four, or 4U, of these clusters were treated during
their life histories. Figure 19 is a histogram showing, for those
treated clusters, the distribution of the time seeding began with
respect to the time of first appearance of a 45-dBz radar echo. Half
of the 74 were treated pri or to, or withi n 7 mi n after, rea chi ng
45 dBz. Thus, only about 20% of the 180 clusters were seeded within a
reasonable time with respect to reaching the 45-dBz threshold.
Figure 20 shows the same kind of diagram using ,1 50-dBz threshnlli
to identify clusters within a district that pose a hili I threat and
should be subject to seeding operations. Of the 128 clusters that
exceeded 50 dBz, 63 (or 49%) were treated. Of those 63, about 591.
-25-
-26-
BOWMAN
SEEDING
150 EVENTS
100
I I III III
-
50 L,
§ 8 ~ ~ 0 § 0 0 0 0 0 0 § ~ 0 g S 0 ~ 0 0 ;i; " " HOUR OF DAY (MDT)
~lL: Distribution of 1980 HOGMP seeding events for District I.
by time of day. An event can be either a flare or a 3-min acetone
generator burn.
200'rTTT.."rrrTTT.."rrrTTl-.:;:rrrTTl
PARSHALL
SEEDING
EVENTS
ISO
~
w 100
o
~
HOUR OF DAY (COT)
~.!!: Distribution of 1980 HDCHP seeding events for District II.
by time of day.
-27-
NO. SEEDING EVENTS
40.,"------'-----'·'--'7--"2"---31~4'--'-'2'--"-5-"'"1
N::::74
30
::l
~
.".. 20 o
.E!i:..!!: Histogram show1ng the distribution of timeliness of initial
seeding events for ha1l suppression using a 45-dBz hail threat thres­hold.
Those cases to the left of zero (min) represent clusters
treated before reaching 45 dBz and those to the ri ght. after.
NO. SEEDING EVENTS
40 0 6 10 2{l 12 7
N::::63
30
*~ 20 ... o
;f. (early) (late)
10
-60 -30 0 30 60
TIME SEEDING BEGAN- TIME Ze~ 50 dBz (min)
~~: Same as Fig. 19. but for i\ 50 dBz tt"lreshold.
-28-
were seeded prior to, or within 7 min of, reaching 50 dBz. Thus
about 30% of the eligible cluster echoes reaching 50 dBz were treated
within a reasonable time.
Close examination of the data revealed that, in most situations,
seeding operations were being carried out on the lI"Ost threatening
clusters. For example, if three clusters rret the NDCMP seeding cri­teri
a at one ti rre, the ai rcraft operat ions were usua 11y concentrat i n9
on the strongest, largest cluster, or two; the weaker one(s} were
neglected by choice. That accounts, in part, for the relatively large
proportion of "hail threat" clusters that were never treated. The
analysis clearly suggests that the limited number of ava11ahle air­craft
prevented the operations personnel from responding to every
threatening storm. Therefore. selections were made and the priority
given to seeding the greatest hail threats. Many echoes meeting hail
threat criteria were consequently left untreated, and many more rain
increase opportunities were ignored in order to carry out the first
NDCMP seeding priority -- treating the roost serious hail threats.
The NDCMP Operations Manual calls for both radar echo intensity
and height criteria to be considered in identifying storms for hail
suppression seeding. Therefore both maxilTMJm echo height data and
reflectivity data were next considered to identify the hail threats.
Figure 21 shows a distribution of the seeding response times for echo
clusters that equaled or exceeded dual thresholds, with equivalent
radar reflectivity factors of 45 dBz or greater, and echo tops at or
ahove 35,000 ft (~10.7 km). Of the 110 such clusters that were iden­tified
from the data for the 11 days in 1982 and only 20 days in 1981,
51 (or 46%) were treated. Of those 51, 69% were treated "on time;"
that is, prior to, or within 7 min of. reaching these combined thres­holds.
Thus, about a thi rd of this total sample of "hail threat"
clusters were treated on a timely basis. This selection, according to
criteria closer to those used operationally in the NDCMP, reflects
somewhat more fa vorablyon the res pons i veness of the hai 1 suppress ion
seeding.
If the criteri a are ra i sed to Ze .. 50 dBz and MEH .. 35,000 ft, the
seeding response remains about the same. Of the 96 such clusters
identified, 45 (or 47%) were treated; of those treated, 71% were
treated "on-time" (Fig. 22).
4,3 ~Studies
The frequent close proximity of seeding aircraft, clouds with
radar echoes, and clouds without echo makes a similar assessment of
the timeliness of rain increase seeding responses in the NDCMP almost
ilJ1lossible, Subjective assessment by personnel observing the seeding
operations from the radar suggests that. in roost cases, some radar
echoes were observed prior to aircraft launch. However, IOOSt clouds
that produce early radar echoes are relatively inefficient and short­lived.
Other clouds usually develop in the same area later, and the
first ones can properly he considered a kick-off signal for seeding
operations.
-29-
NO. SEEDED CLUSTERS
40.,...::'-..:....----"-.::.·-',,·'-"-·-"-,----''-----'--,
N=51
X=-5min.
30
'w"
'e'" repro­duced
here. However. some general conclusions that were reacl1ed
on the basis of those case studies can be presented:
a) The NDCMP operators often had seeding aircraft working
in the general vicinity of the ecl10 clusters that were
observed. Seeding was conducted in the vicinity of the
echoes, and at about the proper time. The pos1tioning
accuracy in both the ai rcraft and radar data was not
sufficient to determine. in fine detail. the accuracy
of the seeding locations (e.g., with respect to inflow
areas or other features that mi ght be apparent in the
radar echo conf1gurations).
b) When seeding was intended for rain enhancement, it often
began rather late. generally after a radar echo had
already developec1. Much. if not roost. rain enhancement
seeding should be directed at cloudS that have not yet
produced an echo. "lost of the seeding in the time frame
for which these case studies were carrfed out was
restricted to hail suppression. so that could account
for the seemingly rather late initiation of the seeding
treatment in many of the cases. However, a general
iJ11)ression of late initiation of seeding for rain
enhancement persists. Certainly practical problems
such as nnving the aircraft into position, locating
inflow areas. and the liKe affect the timeliness of
the seeding, but they are difficult to evaluate in
a .E.21!. hoc analysis.
c) A seedi ng treatment was not often followed by immedi ate
signs of growth in echo area, height, or intensity. In
fact. a decrease in one or rore of these indicators was
not unconmon. However. an hour or so later evidence
often appeared of new echo growth or resurgence. The
physical rrechanism by which a delayed-action response
could occur presumahly involves some l'lynamic process,
or perhaps a microphysical carryover process, but the
details are not clear. Simililr behavior was observed
-31-
in the FACE-l experiment ("oodley et al •• 1982), wtlere
dynamic effeds were an im)ortant par'tOf the conceptual
lllOdel. (It is fair to not~ here that this behavior was
not confi rmed in FACE-2.) These indications warrant
some further investigation.
The NOCMP Operati ons Manual req 11 res the operators to gi ve fi rst
priority to seeding for hail suppreisfon. Consequently. seeding
opportunities for rain enhacement my be missed, or attack.ed late,
because the aircraft are busy with lligher priority activities.
Section 4.2 discussed the responsiv,mess of the NDCMP operators
to hail suppression opportunities.
5. RESULTS OF RADAR DATA MAlYSES
Much of the project effort was devoted to analysis of the radar
data for cl1matological information (already discussed in Sec. 3),
fundamental knowledge. and possible indications of seeding effects.
This section discusses some of the main results of this analysis,
inclUding the deYelopment of relationships between various echo
cluster characteristics.
5.1 Relationships Between Echo Height and Rain Volume
Oynamic effects of seeding have heen indicated in studies of
various randomized cloud seeding projects in the Dakotas (e.g., Dennis
and SChock, 1971; Dennis et al., 1975b). Evidence of dynamic effects
is sought most directly fri-cloud top or maxilllJm radar echo hetght
data. In a randomized experiment. the heights of seeded and non­seeded
cloudS can be cOlTllared either directly or with the aid of a
suitahle cloud oodel (e.g •• SilTllson et a1., 1967) to elucidate any
differences attributahle to the seedTng-:--ln a non-randomized opera­tional
project. such cO"'ilarisons become IlOre difficult. SIllith ~&
(1973) co~ared radar max;lTlJm echo height observations within and
outside the target districts of the South Dakota state weather IOOdi­fi
cat i on project and found some indi cat ions of differences consi stent
with the ell:pected dynamic effects of seeding. However. there were
other possible explanations for the differences and the results were
not conclusive.
One way to establish a physical relationship that is consistent
with the expected dynamic effects of seeding is to examine the rela­tionship
between maxil1lJm echo height and rain volume. The physical
argument begins with the release of latent heat invigorating the
vertical growth of the clouds which, in turn, produce greater rain­fall.
Such an analysis can establish the existence of an echo height­rain
volume relationship but cannot prove that seeding increases
either the heights or the volumes. The results may therefore be
conststent with, without being conclusive of, dynamic effects of
seeding.
The question can be approached in different ways. The low-ti It
radar data acquired on a given scan can be used to determine a volu­metric
rain rate (m3/hr) for each cluster, and tl10se rates can then
be compared with tile observed maximum echo heights (MEH). The height
values can be taken from the same scan. or from a preceding scan in
order to allow for the time requ1red for the precipitation to descend.
Alternatively. the rain volume can be accumulated over the duration of
each cluster and the total amount then co""ared with the maxilllUm ecl10
height found during the entire cluster lifetille. Intermediate ver­sions
are 111 so possible with the cO"'ilarisons being made between rain
volumes and maximum echo heights for some fill:ed time interval, say.
one hour. There are some indications (Oennis et /11 •• 1975a) that
cloud depth (MEH minus cloud base height) mighthelletter correlated
with the rain volumes than is the MEH alone.
-33-
Most of our work was with the MEH and rain volume values for the
entire cluster lifetimes. Values presented from the 1980 and 1981
data in earlier reports (Smith et a1.. 1982, 1983) are suspect because
of questions about the antenna elevation angle data. Therefore we
emphasize here the MEH-rain volume relationship found for the 1982
data (Fig. 23). The rain volumes are plotted on a logarithmic scale
because of the wide range of values involved. Work by Gagin (l980)
suggested that the echo heights could be plotted on a logarithmic
scale as well. In fact. the correlation turns out to be slightly
higher (0.836 vs. 0.833) if that ;s done. The standard error of
estimate of the logarithm of the rain volume is also somewhat lower
(0.61S vs. 0.619) if the logarithmic height scale is used. However.
the differences are so small as to be of little real significance.
Table 5 presents the parameters of the rain volume-MEH
relationship for this data set. There is not iTlJch evidence in
Fig. 23 of a difference between the points for seeded and non-seeded
clusters. so separate regression parameters are not presented. The
DICKINSON 1982
EE
~w
3 3
g
~ 2
~o"
g
'UNSEEOEO
@SEEOEO
" ../:'.....
: :~:: .' (...... ": .
,..' .,.·f
o ..,,
'0 ~ %. ~
.. :.::~ '. 0. • 0
. · .. .'0.·
-1
0
L -~-~--~-~-~-J12C-~~-''-:6-~---:20
MAXIMUM ECHO HEIGHT (km)
~Q: Scatter plot showing the relationship between maximum radar
echo heights and radar estimated rainfall volumes for the 1982
clusters. Twenty-eight clusters were seeded and 323 were non-seeded.
-34-
TABLE 5
Regression Parameters for Cluster
Echo Height - Rain Volume Relationships
(1982 Dickinson Radar Data)
log V log V
vs. MEl~ ~
logarithm of Rain Volume:
Mean 2.084
Standard Deviation 1.118 (same)
Coefficient of Variation 0.54
Maxilrom Echo Hei ght:
Me,n 8.50 km 0.895
Standard Devi at i on 3.31 km 0.178
Coefficient of Variation 0.39 0.20
Regression Parameters:
Correlation Coefficient r 0.833 0.836
r' 0.694 0.698
Intercept -0.312 -2.604
Slope 0.283 I:.m- 1 5.238
Standard Error of Estimate 0.619 0.615
correlation coefficient is about the same as that found in Project
Cloud Catcher (0.85; Dennis et al •• 1975a), and the relationship ;s
consistent with the idea thattiller clouds tend to produce rrore
precipitation.
A linear relationship on a semi-log plot like thdt in Fig. 23
indicates an eKponential relationship between the variables, of the
form
Rain Volume V = A x lQb{MEH)
Values of the intercept parameter log A and the slope parameter b of
this relationship appear 1n Table 5. They indicate that each incre­ment
of 1 km in lM.xiRl,lm echo height produces approximately a m
increase in the cluster rainfall (antl1og of 0.283). Therefore this
basic tenet of the dynamic seeding hypothesis seems to be supported
by the observations from tforth Dakota.
-35-
In the 1981 data. the overall cQrrelation between log V and MEH
was weaker (0.668; Smith et a1.. 1983). That may be due largely
to the possible elevationan'9f'e dat-l. quality problem III?ntioned in
Sec. 2.1. However, when cO"lluted by mnths (June. July. August).
there was a general increase in the correlation over the surrwner
(0.60. 0.71, 0.80). This suggests 'Will! significant variations in
the general weather patterns during the summer of 1981. June was
relatively cool and wet with an abundance of stratiforlll systems
havi ng sorre embedded convect i on. The observed maxiflRJm echo hei ghts
were generally less than 10 km. However. in July and August. the
weather was Il'Ore convective and the majority of systems exceeded
10 km in height. The greater range of MEH's probably contributed
significantly to the increase in thp. correlation.
Another factor that may contri bute to the di fference between the
1981 and 1982 correlations is the way in which we defined the echo
cluster entities. In our classification. a cluster may contain any
number of individual cells. and roost contain more than one cell. This
means that the horizontal dimensions of our clusters are essentially
unrestricted; however, the vertical dimensions are IlOre limited (e.g ••
by the tropopause height). To give an illustration, suppose that a
cluster contains four identical cells. each having an MEH of 10 km.
If one were to define each cell as a separate entity, each cell would
be about one-fourth as large and produce one-fourth as l'IUch rain as
the overall cluster, yet all of the MEH values would be identical. If
every echo cluster contained the same number of identical cells, one
would expect the ·cluster" height-volulII? correlation to be the same
as the ·cell· correlation. The main difference WQuld be that the
clusters would produce rrore rain, by a factor equal to the number of
cells contained. However, if il. single cell dominates the rain volume
for each cluster, this lTIJ1tiple-cell problem would be considerably
moderated.
The average cluster identified in 1981 was si9nificantly larger
than the average 1982 cluster. This may mean that the 1981 clusters
tended to contain rrore cells, on the average. than the 1982 clusters.
which could tend to produce greater scatter in the log V-HEH
relationship.
5.2 Relationships Between the Area-Time Integral and Rain Volume
The echo cluster data were used to study the relationships between
a llEasure of echo size and duration called the Area-Time Integral
(ATl) and the rain volume (Ooneaud et al.. 1984b). The rain volume V
over an area A during the time T is-gfven by
V'~JARdadt (1)
where R is the rainfall rate. If R were a mnstant. Rc ' (1) could
be written as
v • RC~~ da dt (2)
The All Is the double integral in (2). which in analyzing data can be
approximated by a sum:
All '" .Irri da dt .. L: Ai .6.ti A i
(3)
Here. Ai 1s the area over which rain was detected during the i th
observ1ng per10d and .6.t1 is the time interval between observations.
The ATI and the rain volume are given here in km2 hr and km2 fI'Ill.
respectively. These quant1ties are distributed roughly log-normally
(cf. Sec. 3.5).
The ATI concept is useful because it 1ncorporates. in a s1""le
way. information about the areal extent and the duration of the
precipitation events. The ATl calculations can be made for fhed
areas on the ground. as in the work reported 11'1 Doneaud et a1­(
1981). or for roving storm systems (echo clusters) as {i\t~
present work.
With radar data. the value of the echo area at any given time
depends strongly upon the reflectivity factor threshold employed, so
the All values have a simili!lr dependence. The amounts of rain asso­ciated
with regions of low reflectivity are relatively small. however
(see Sec. 3.6). so the rain volumes are lIlJch less sensitive to the Ze
threshold. The choice of the IOOst appropriate reflectivity threshold
for calculating the area-time integrals 1s therefore partly subjec­tive.
After considering the factors discussed in Ooneaud et 031.
(1984b). we settled upon a 25-dBz threshold for determiningt~AlI's
from radar data. at least for the semi-arid type of climate of North
Dakota.
Figure 24 illustrates the fact that scatter plots comparin9 the
cluster rain volumes and area-time integral values expressed on loga­rithmic
scales show strong correlation. The correlation coefficients
and the logarithmic standard errors of estimate are roughly 0.98 and
0.16. respectively. The latter implies a one-standard-dev1ation
scatter in the rain volume estimates of a factor 1.45. In percentage
terms. the corresponding range is between +45 and -31%. That is com­parable
to the uncertainties which typically occur in rain volume
estimates obtained from radar data in the usual manner, employin9 Z-R
conversion followed by space and time integration (e.g •• Atlas. 1964).
The points for seeded clusters have been circled in Fig. 24. One
evident difference between the seeded and non-seeded clusters ;s that
the former tend to be concentrated toward the upper part of the plot.
-37-
Io'r------,----"=c=="-T=---,---,
• NON-SEEDED CLUSTERS (5081
o SEEDED CLUSTERS (75
CORR. COEFF ~ 0.98
101 10 103 10" 105
25dBz-AREA TIME INTEGRAL (km2hr)
~~: Scatter plot and regression line of echo cluster rain
volumes VS. 25 dBz area-time integrals for 1981 Bowman data.
Points for seeded clusters are circled.
This is due largely to a "selection bias" inherent in the NDCMP
operations. where first priority ;s given to seeding the larger
storms for hail suppression.
Figure 25 shows a similar scatter plot for the 1982 radar data
from Dick.inson. The correlation coefficients and regression param­eters
for these plots are quite similar from year to year (and even
from II'(Inth to mnth with; n a 9; yen year). There may be some small
differences associated with climatological variations, but there is
little reason to doubt the general consistency of the ATI-rain volume
relationship. That relationship can be expressed in tile form of a
power law ilS
v " K (AT!)b
with an exponent b that is not far from unity.
-38--
(4)
lOG (ATI)
~~: Scatter plot comparing radar-estimated ra1n volumes from
echo clusters with their area-time integrals (volumes in km2 lTID,
All values in km2 hr). Points for seeded clusters are circled.
Oata frOOl Oickinson. NO, radar, 1982.
To test the consistency of the rain volume versus area-t1me
integral relationship further, the parameters for (4) derived from the
1980 HOCMP radar data were applied to the 1981 echo cluster ATI values
to estimate the corresponding rain volumes. Those estimates were then
cOlllJared with the radar-estimated rain volumes co~uted in the usual
way. using a Z-R relationship to obtain the rainfall rates followed
by space and time integration.
Figure 26 illustrates the results for a sample of the 1981 echo
clusters. The agreement is fairly good, but the least-squares line
(dashed) is inclined slightly to the y ,. X line. The dashed line,
therefore, indicates a slight tendency for the 1980 for11lJla to
overest1mate the 1981 rain volumes for small ATI values and under­estimate
them for large ATJ values. That may be caused, in part, by
the different weather conditions 1n the project area between 1980
and 1981. However, the 1980 data included very few seeded cases, so
the 1980 forlll.lla can be considered as essentially one for un seeded
clusters. The fact that it underestimates the rain volumes for ITI:)st
of the 1981 seeded clusters could then be tak.en to suggest a positive
effect of the seeding upon the rain volumes.
-39-
~ 1Q5,--,-__,-__-,-,_-,-,-,...", 1. .NON-SEEDEO CLUSn RS "
~ 0 SEEDED CLUSTERS
~ 104 - - - REGRESSION LINE
~"
~ 10J
:>
~r'5:l '10
1
'"~ ~ 10I°OO',;'------.J'O';-,---',o"c;----"o,..,--'-'-0':---"0'
~ RAIN VOLUME ESTIMATED FllOM 19BOFORMULA lkm2mm)
~!!: Colll'arison of echo cluster rain volumes c0lll'uted from Z-R
conversion and integration with corresponding volumes estimated from
the 1980 rain volume ys. 25-d8z ATI forllUla. Data from Bowman radar,
July 1981. The solid line is the y • x reference line indicating
perfect agreement, wtli Ie the dashed line is the regression line.
Points are shown for all seeded clusters, but only every other
non-seeded cluster.
5.3 Average Rainfall Rates During Storms
The average rainfall rate, R, for an echo cluster is given hy
the ratio of the total rain volume IV) to the dred-time integral
(ATI),
R • V/(ATI) (5)
If V is 9iyen in km2 l1I1I and the ATI in km2 hI", their ratio has units
of lIIlI hr-1• This ratio is the aye rage rainfall rate oyer the life­time
of the cluster and oyer the are" with rain (Ooneaud et a1.,
1984a). Using radar data, this average value will be senSitive to
the reflectivity threshold used ;n the ATI computation.
-40'
The average rain rate considered over short periods of time (i .e.,
minutes) exhibits large variations during convective storms. However,
if the average is computed over longer time intervals, or particularly
over the lifetime of a storm. it shows truch less variability from one
storm to the next. As the time period included in the computations
comes closer to the total storm duration, the scatter of the average
rainfall rates is reduced.
From (5) and (4) we can obtain:
R:: K(ATI )b-l (6)
Since b .. I, the average rainfall rate R is almost independent of the
size and duration of the storm. as indicated by the ATI. Because the
ATI-rain volume relationship seems to vary little from year to year,
R should also change little from year to year. Thus, a comparison of
cluster rain volumes computed from the 19B1 radar data using a) the
overall average rainfall rate found in 1980; 25 dBzl. maximum reflectivity factor. or maximum
echo height as the dividing point. On the average, a cluster reached
its maximum development after about 56% of the total cluster lifetime.
Separate average rainfall rates were then computed for the growing and
decaying periods. The average rainfall rate for the growing period
exceeded that for the decaying period by an average of ~20% (Fig. 27).
This can be compared to the subtropical climate of south Florida,
where the rainfall rate for the storm growing period was found to
average about twice that for the decaying period (Griffith et a1.,
1978). --
A trultip1e linear regression analysis demonstrated that the radar
estimated rain volume from a cluster is well correlated with the
maxitrurn single-scan volumetric rainfall rate. That suggests the
possibility of estimating the total rain volume for a storm imme­diately
following identification of its maximum stage of development.
This could improve rain volume estimates from satellite data because
larger errors might be encountereo in such calculations due to
overestimation of the rain volumes during a storm's weakening or
decaying phase (due to cirrus dehris).
-41-
-G~OWINGPE~IOD
--DE(:AVINGPEAIDD
•,f----;------t------''''---ii02=:=;,iF-------..J
AVERAGE RAIN RATE (mm hr-')
~ll: The relative frequency distributions of cluster average
rainfall rate for growing and decaying periods.
5,4 Correlations Between Echo Cluster Characterist~
Correlations between several pairs of the identified radar echo
cl uster characteri sti cs were computed. The pu rpose was to i dent i fy
those characteristics that are IOOst closely related to the rain
amounts as well as to explore the extent to which the various
characteri st i cs can be considered independent. Tabl e 6 1i sts the
characteristics included in this investigation, together with average
values for the 1981 (Bowman) and 1982 (Dickinson) data sets. Some
differences are apparent between the two years; they are due IOOstly
to interannua 1 vad aU ons plus the absence of 1ate-summer con vect i ve
storms from the 1982 observations.
The aforementioned possibility of elevation angle errors in the
1981 data may also have contributed to the di fference in average maxi­mum
echo heights. Partly because of that problem, a complete corre­lation
matrix is presented only for the 1982 data (Table 7). As
previously noted, the strongest correlation by far is that between the
area-time integral and the rain volume. However, several other pairs
of variables exhibit good correlations. The maximum reflectivity fac­tor
(ZMX) is well correlated with all of the other variahles. This is
reasonable hecause intense storms tend to be larger. last longer, ilnd
produce IOOre rain.
~4Z-
TABLE 6
Average Values of Some Echo Cluster Characteristics
".Jmer of clusters
Haxlllllm echo height, MEH (km)
Maximum reflectivity factor, ZMX (dBz)
Echo durat ion, T (hr)
Area-t1me 1ntegral, All (km2 hr)
Rain volulIIe. V (km2 1lI1l)
Average rainf1l1l rate, R (rrm/hr)
*Geometr1c mean values.
TABLE 7
5B3
9.85
43.9
1.36
79.8*
350*
4.45
351
8.59
41.2
32.8*
131*
3.50
Correlat10n Coeff1cients Between Echo Cluster
Characteristics for the 1982 Radar Data
log R
log V
10g(ATIhs
ZMX
0.577
0.646
0.833
0.798
0.771
0.578
0.663
0.836
0.798
0.790
-43-
0.820
0.887
0.B56
0.800
0.482
0.565
0.978
0.571
0.616
The good correlation between ZMX and the cluster average rainfall
rate (It') suggests that one could es~imate the average rain rate from
the maximum reflectivity factor wit'l reasonahle confidence. That
fnformation might be used along Wit.l the AT! to get il slightly
improved rainfall estimate by takin'l
v " R(Z) x (AT!). (7)
The good correlation between ZMX and log V is also reasonable. More
intense storms tend to produce IOOre rain, and to calculate Vane rust
use some Z-R formula relating reflectivities to rainfall rates.
The good correlation between ZMX and MEH is also plausible in
light of previous work. The 0.77 correlation found for 1982 is
sli9htly better than the 0.72 obtained for 1981, but that difference
may be related to elevation angle elTors in some of the 1981 data.
Similar height reflectivity correlations were noted in radar obser­vations
associated with operational cloud seeding in South Dakota
(Smith li.!L., 1973), at least for the un seeded echoes.
The sample of seeded clusters in 1982 was small (28 cases), but
some overall comparisons can, nevertheless, be made hetween the seed
and no-seed groups. Table 8 comparf'S the average characteristics for
the two categories; the selection bias. accentuated in these data by
the restrictions on seeding for rain enhancement in 1982, is evidellt.
Tab 1e 9 compares the carrelat ions between v':! ri ous radar vari ab1e pa irs
for the unseeded and seeded groups. Most of the correlations are Il'KJch
weaker in the seeded group. Some of this can be attributed to the
fact that the range of values for the seeded clusters was rather
small, as was the sample size. However, it is encouraging to see that
the main correlations, such as those between rain volume and AT! or
maximum echo height, remain strong pven for the data sub-sets.
5.5 Rainfall Comparisons
To evaluate the effects of seeding upon precipitation, one would
ultimately like to compare the rainfall in the seeded target and
unseeded areas. The present radar data do not permit this because
continuous coverage was not available. However, the radar estimated
rain volumes for the echo clusters were subjected to a seed/no-seed
comparison. Figure 28 shows the frequency distributions of seeded and
unseeded cluster rain volumes (with the latter included whether or not
they were inside a target district) for the 1981 Bowman radar data.
This straightforward comparison of the frequel1cy distributions of
the rain volumes from seeded and non-seeded clusters shows that the
former tend to have considerably greater rail1 volumes. However, that
difference is due largely to the previously mentioned selection hias
operating in the NDCMP: priority is given to seeding larger storms
1,/,.
TABLE 8
Compar1son of Average Values of Some Characteristics
of Seeded and Non-Seeded ECh'O Clusters (1982 Oata)
Seeded Non-Seeded
Number of clusters 28 323
Haxiflllm echo height. MEH (km) 13.3 8.2
Haxirum reflectiv1ty factor. ZMX (dBz) 51.1 41.0
Area-time integral. AT! (km l hI") 562.3* 25.6*
Rain volume. V (kmZ urn) 2965* 100*
Average rainfall rate. R (nInthI') 4.71 3.40
*Geometric mean values.
C- _
for hail suppression. A greater proport1on of the l"rger. more
intense clusters 1s therefore seeded and the apparently greater rain
volume is mainly a consequence of that selection process. Conse­quently.
the selection bias renders this type of comparison invalid
for estimating the effects of seeding on rainfall. The only conclu­sion
one can glean from Fig. 28 is that the NOCMP seeding operations
are. indeed. carried out in the manner prescribed in the Operations
Manual. that is. by giving first priority to seeding the strongest
storms for hail suppression.
An attenvt was made to circumvent this selection bias by
class1fy1ng the clusters as in or out of a target district. Clusters
passing across any part of an NDCMP district at any time during their
life histories were classified as "in-district." The thought was that
all of the clusters in a district should be candidates for seeding.
while those clusters outside the district are not SUbject to treat­ment.
The NDCMP operators can be presumed (at least under idealized
conditions) to treat the in-district clusters in the manner roost
appropriate for enhancing the in-district ra1nfall. If this presump­tion
were valid. one would have a reasonable datil set to which target­control
statistical co""arisons could be applied {assuming. of course.
that seeding 1n the district would oot. or did not. influence the rain
-45-
TI\BLE 9
COqJarfson of Correlation Clefficients Bet~een Echo C1'lster
Characteristics. for See:led and Non~Seeded Clusters
(1932 Data)
Variables ... ~ ~!l ...l!1L log{ATlhs ~
R 0.428 0.425 0.652 0.187 0.287
(0.567) {0.566l (0.829) (0.479) (0.572)
log R 0.476 0.474 0.759 0.116 0.352
(0.643) (0.6541 (0.891) (0.562) (0.617)
log V 0.797 0.786 0.605 0.980
(0.808) (0.813 I (0.854) (0.975)
log(ATI)2s 0.765 0.763 0.611
(0.768) (0. nIl (0.791)
ZHX 0.555 0.560
(0.767) (0.778"
*Top nUl1ber. seeded clusters; bottom nU'Ilber (in parenthesesl.
non-seeded clusters.
production outside the district). -f this presumption is not valid.
the main effect would be to dilute lhe apparent effects of seeding in
any in- versus out-of-district cO~irison.
Such a cOqJarison of the cluster- ra1n volumes shows a tendency
toward greater ra i nfa 11 from the in-di str; ct cl usters (Fi g. 29).
However, it appears that another kird of bias may be mainly respon­sible
for that apparent difference. Small echo clusters will be
distributed over the map rore or less at random. and therefore (after
appropriate adjustment for the area5 involved) would be about as
likely to fall within as outside a district. Large clusters. on
the other hand, tend to have longer lifetimes and cover !OOre area
as they move. They are therefore more 1ikely to cross over some
part of a district. Consequently. the larger clusters are rrore
likely to be placed into the "in-district· category. leading to a
bias in the in- versus out-of-district cOfl)arisons. That
"classification bias· therefore renders this approach to the
rainfall comparisons invalid as well.
-46-
SEEDED
CLUSTERS "----'r-1751 , ,
,,,' \,
" \
I \
\
,
,,
101 !if 103 104 105
RADAR ESTIMATED RAIN VOLUME (km2 mml
~~: Co~arlson of frequency distributions of the radar estimated
rain volumes for seeded and non-seeded echo clusters. for the 1981
Bowman data.
trwr
301---.------';~~~~~~'---_r--__,
ro
rw
> 10
~g ~~: Co~arison of frequency distributions of the radar estimated
rain volumes for in-district and out-of-dfstr1ct echo clusters; 19iH
District t (Bowman) data.
-47-
These approaches, while of interest, have therefore failed to
yield acceptable evidence about possible effects of seeding on the
precipitation. The comparisons are not inconsistent with the intended
effects of the seeding, but the selection and classification bias
probl ems prevent drawi ng any substant i ve inferences. An al ternat i ve
approach woul d be to use long-term tot a1 ra i nfa 11 compari SOr'lS, but the
mode of radar data acquisitior'l used in this investigation is not
sui tab1e for such compari sons because the coverage was not cont i nuous.
Therefore such work wi 11 have to be done wi th the ai d of avai 1able
rain gage data from the project area.
5.6PossibiJ..!..1.t.Q.f.~~.i!ltheObservations
With a 20 antenna beamwidth, questions may arise about possible
range variations of the radar rreasurements (e.g., Wilson, 1975;
Collier, 1984; Zawadzki, 1984). Even though data were recorded out
to about 275 km range, the North Dakota radar data were analyzed
only to a range of 145 km. That mitigates some of the difficulties
encountered at longer ranges.
To investigate possible remaining range effects, we used several
procedures. Fi rst, the maximum equi va 1ent refl ect i vi ty factor data
for the echo clusters observed at Bowman in 1981 were divided
according to whether the observations occurren "near" or "far" from
the radar. The di vi di ng poi nt was taken as r '" 105 km, whi ch di vi des
the useful area of radar data into two approximately equal parts.
Figure 30 shows frequency distributions of the maximum reflectivity
factors for these two subsets of the data. The di fferences are not
large, but there is a general tendency for the observed maximum
reflectivity factors to be smaller for the more distant echoes. Such
a difference could be explained by beam filling problems at long
ranges, with occasional contributions due to attenuation by inter­vening
storms. This finding suggests that any results involving the
refl ecti vity factor observat ions shaul d be vi ewed with some cauti on.
Second, "Ie prepared a "two-dimensional frequency distribution" of
observed cluster maximum equivalent radar reflectivity factors (ZMX)
as a function of range from the radar. Figure 31 shows this distribu­tion.
The decrease in radar sensitivity with range is evident because
the minfllllm ZMX values tend to increase with increasing range. In
addition, the behavior at the top of the plot shows that the maxirrum
ZMX values decrease with range. For exa~le, no values .. 60 dBz were
observed beyond 120 \::m. One may be tempted to extrapolate Fi g. 31 to
infer that at some sufficiently long range (perhaps around 250 km) all
of the ZMX observat ions wi 11 be about the same, i.e., around 40 dBz.
These fi gures show that there are range effects on the ZMX
measurements related to the change in radar sensitivity and in
beam-filling considerations with increasing range. These
observations suggest that:
-48-
~~~
RANGE >105 km , ....
(226 CLUSTERS) ,/
/
,/
I
I
I RANGE < 105 km
I (351 CLUSTERSl l
100r----,--.-T=T=-r-=i:?"~t::"'_,.-__,
190
~ 80
~ 70
2 60
~ 50
~40
;::
~ 30
~ 20
u 10
~~: ColTJtlarison of frequency distributions of the maxil1l.lm
reflectivity factors for nearby (I' < 105 km) and IOOre distant
echo clusters; 1981 Bowman data.
60 80 100 120 140
RANGE (km)
40
0 1 2 1 0 0 0 0 0 0 0 0
1 3 5 3 1 2 3 1 2 1 0 0
1 ,. 3 4 1 1 • • 4 5 5 2
2 14 • 2012 11 11 14 14 • • •
5 • 12 • 14 13 • 10 ,. 14 14 14
4 3 1 12 10 13 15 20 ,. 11 19 25
3 3 1 • • ,. 15 12 18 ,.17 14
2 5 8 • 1 • 12 11 22 13 ,.8
0 2 2 5 5 3 • 4 13 3 4 4
0 2 0 1 1 1 0 0 0 0 0 0
20
20
f.!..9.:.11: Two-dimensional frequency distribution of maxilllJll\ equivalent
radar reflectivity factors for 857 echo clusters as a function of
range from the radar (l981 Bowman and 1982 Dickinson data).
-49-
1) Project flJ2'teorologists should not rely on the radar for
detection of initial precipitation at long ranges.
2) Distant storm intensities ",ill he underestimated, which
may lure the meteorologists into an erroneous assessment
of the threat that the storms pose.
Third, we compared the distributions of reflectivity factor values
in the low-tilt data for the in-district versus out-of-district
cluster classifications. To account for range variations in the bin
size in the data grid. the distributions were determined with respect
to echo area. Figure 32 shows the distributions for the 1981 Bowman
data. when the radar was located within District I. A tendency for
more of the echo area for the nearby in-district clusters to be asso­ciated
with low reflectivity factors is evident. The explanation lies
in the greater sensitivity of the radar set for echoes at short range.
This factor may contribute. in a small way, to the in- versus out-of­di
stri ct di fferences noted in Fi g. 29, but the cl ass i fi cat ion bi as
is still likely to be the major contributor.
In 1982. the research radar was located at Dickinson for a variety
of reasons, including the desire to get fOOre nearly equivalent obser­vations
of in- and out-of-district clusters. The areal distributions
E!.!h.E: Comparison of cumulative frequency distributions of
reflectivity factor values based on areal coverage for in­district
and out-of-district clusters; 1981 Bowman data.
of reflectivity factors for 1982 appear in Fig. 33. Evidently the
relocation of the radar was successful in this respect, because the
difference hetween the distributions has essentially disappeared in
the 1982 data.
Finally, to investigate possible range dependence of the maximum
echo height (MEH) observations, we divided the observation area into
10-km range increments and examined the MEH observations from each
10-km annulus (Fig. 34). The upper limit on the scan elevation angle
obviously restricted the MEH obServations for nearby storms (the upper
limit was usually 12° in 1982, but on a few occasions scans were
recorded up to 15°). Apart from that, there does not seem to be f1lJcll
systematic variation of the MEH observations with range. This
suggests that IOOre confidence can be placed in results based on
the echo top observations.
5.7 Oevelopment Q!. Climatological Z-R Relationships for~
Storms 1!!. the Northern Great Plains
The presence of hall in most strong sumlTer-type convecti ve storms
of the northern Great Plains complicates attempts to estimate precipi­tation
from radar data. Hailstones are usually intermingled with
raindrops in the high reflectivity regions of these convective storms
1982 DICKINSON DATA
~
~80
or~40
>
~
~20
8
o'--'-....L-'_:'---'----'-_'--'-----'----'-_'---'
10 10
~~: COllllarison of cumulative frequency distributions of
reflectivity factor values based on areal coverage for in­district
and out-of-district clusters; 1982 Dickinson data.
-:"1-
1982 DICKINSON DATA
NUMBER OF OBSERVATIONS
~
(/J
:;;
~
....
J: "iii
J:
o
J: "W
'::":>
:;;
~
:;;
N ""352
15"/
/
" A
/
10 ~ ~
MEANf..
, /
II MI'N
RANGE(km)
~~: Graph showing observed cluster max;l1l.Im echo "'eights as a
function of range from the radar.
and current operational radars cannot distinguish between the two.
Radar estimates of rainfall amounts from such storms therefore are
often found to be excessively high. A study was therefore made to
develop a convective Z~R relationship for the operational S-cm radar
systems used on the NOCHP by employing the climatological approa"'l
developed by Miller (1971). Calheiros and Zawadzki (1983) used
"non-sil1lJltaneou5 radar and rain gage rainfall data" in a similar
way to develop a reflectivity-rain rate relationship for Brazil.
Smith et a1. (1975) have developed a regiona1 Z-R relationship
intended ~account for the effects of hail by collllaring lO-cm
radar data collected on the North Dakota Pi lot Project (NDPP)
wi th gage data us 1ng 11 di fferent opt i mi zat ; on approach.
Only equivalent radar reflectivity factor values> 42 dBz were
considered in this analysis. because it focuses on situations where
hail is likely to be present. A threshold of 42 dBz corresponds to a
rainfa 11 rate of about 15.2 nrn h-1 us lng the well-known Z '" 200 RI. 6
relationstdp. ThuS. smaller rainfall rates and Ze vallJes were not
considered in constructing the cUlllJlative frequency didgrdms found
herein.
-'Jz-
The vertical section of a shower rer>resented by a 2° beamwidth
radar observation is comparable to that sampled during a 5-min surface
rainfall observation. Sho~lers usually occur with weak to moderate
downdrafts. and the rain (with hail intermixed) typically falls at
an average speed of about 10 m S-l. At this rate •. the precipitation
will fall about 3 km in 5 min. A 2° radar beam is about 3 km wide
at -85 km distance. Thus the radar observations can be considered
reasonably comparable to 5~min rainfall events.
In 1982. volume scans of convective storms were made at
approximately 6-min intervals using the 5-cm radar system located
at Dickinson. The radar reflectivity factors> 42 dBz. observed at
the 1° elevation angle for ranges between 60 and 105 km, were tabu­lated;
this range interval was chosen to minimize problems with ground
clutter and the range effects discussed in Sec. 5.6. Figure 35 shows
a cumulative frequency plot of the Ze values thus obtained.
During the period 1957 through 1970, the Newell (SO) Agricultural
Research Station maintained several 6. 12, 24. and 192-h recording
rain gages in conjunction with watershed sturJies (Fig. 36). Data from
90
60
70
60
50
40
30
20
"
~ ~ M
RADAR REFLECTIVITY FACTOR, Ze (dBz)
99
98
97
93
92
"
~~: Cumulative frequency distribution of 5-cm equivalent radar
reflectivity factors> 42 dRz ohtained during 1982 in southwestern
North Dakota.
_5J~
1982
I DI~~~;~)N ~
I 0 • BISMARCK I 1970 ~
I LEMMON ~
~ ~~~__ ~ __N.DA~.:.-
I 0 'E S.DAK.
I ~
~.9J::!.1:.~ ;'."\ ~
WyO. r • 1 !NE~El~ PIERRE
IRAPID CITY
RADAR SITES
NEWELL AGRICULTURAL RESEARCH
STATION WATERSHEDS
o 40 80 160
KILOMETERS
.E..1Jk1!: Map showing the locations of the Dickinson radar site and
the Newell Agriculture Research Station's (ARS) watershed studies,
Radar data from the Lemmon site were used in an earlier study
(Miller, 1971),
several of the gages were selected for analysis. Ei9hty-five statiorl­seasons
of summertime (May through August) 5-min rainfall rates were
tabulated. A total of 1731 rainfall rate values which equaled or
exceeded 0.05 inches per 5-min period (~15.2 fTlll h- 1) were used to
construct the cumulative frequency distribution shown in Fig. 37.
A Ze-R curve was then generated by selecting radar reflecti vity
factors and rainfall rates for corresponding points on the appropriate
cunulative frequency distribution curves. For example, in Fig, 35,
50 dBz is the value below which ~91.8% of the observed reflectivity
factors lie. The rainfall rate distribution, Fig, 37, was entered
at the same frequency level to find a corresponding rainfall rate of
63 om/h. Continuing to plot Ze values against corresponding R values
in this way resulted in a number of points. which were then used to
obtain a least squares Ze-R equation, The rainfall rate (R) was
treated as the predictand using the equivalent radar reflectivity
factors (Ze) as the predictor in establishing the least squares
regression line,
The resulting equation is:
(8)
-54·'
CF%
100
90 99
80 98
70 97
60
95
40 "
30 93
20 92
10 " L.L-L--L-l_l-LL-'----l~.L..L---"---,JOc_-'-"""-.l.~fo_l90
20 40 60 80 100 120
FIVE MINUTE RAINFALL RATES (mm h-')
~E: CUlllJlative frequency distribution of 5-min rainfall rates
observed over the period 1957-1970, using data from the Newell ARS
site (Fi g. 36).
This 5-cm relationship is very close to the well known and widely
used Z = 200 Rl.6. It fails to show 11 crossover between the common
all-rain (Marshall-Palmer) and all-hail (Douglas) relationships as
was found by Miller (1972) and Smith ~~ (l975) using la-em radilr
data. This is likely due to the fact that very high Ze values are
less frequently observed using a 5-cm wavelength because of
attenuation.
These results support the use of Ze = 200 RI.6 in using 5-cm
radar data for estimating convective rainfall in the northern High
Plains. For a lO-cm radar system, however, a different relationship
may be needed; Ze = 200 RI.6 may be used for Ze values < 42 dl1z and
Ze = 25 R2.37 for values> 42 dBz, as suggested in f1i ller (1972).
Alternatively, the single optimized relationship in Smith et al.
(1975) may be used. ---
Table 10 compares rainfall rates calculated for selected Ze
values from the well known Marshall-Palmer forl1l.l1a, the 5-cm
equation developed herein (8), the optimized equation developed for
thi s regi on by Smith et a 1. (1975). and the two-part equat i on from
Mi 11 er (l972). The presence of ha i l. whi ch produces hi gh Ze va 1ues,
is climatologically accounted for with 10-ern radar data by using an
equation developed for the specific area for the convective storm
season. At 5 cm. the equation L
-;;;t:B 'll 'll '6 .;~~
"I :ll :g
Jf~
-56-
This approach to developing Ze-R relationships could be applied
to radars w1th narrower bearrw1dths (e.g •• 1°) by cOlJllarlng the Ie
distributions with 2- or 3-m1n rainfall rate c11matologies. Further
studies using the techniques of Calheiros and Zawadzki (l983) con­cerning
the variation of equivalent radar reflecthity cUIlRJ1athe
probability curves as a function of distance from the radar may
further improve this climatological approach.
-57-
6. NUMERICAL CLOUO MODELING STUOIES
Ewald (1983) evaluated the dynamic seedahi lity of clouds in North
Dakota usin9 the 1500 MDT sounding data taken in 1981 at Baker,
Montana, in connection with the CCOPE project as input to the GPCM
one-dimensional, steady-state cloud lTOdel. (Due to a misunderstanding
about the way the sounding data were archived, the times were erro­neously
reported as 1500 GMT by Ewald and in Smith et al., 1983.)
Changes in cloud top height (bH) and in maximum updraftspeed (lIW)
between natural and seeded versions of the simulated clouds were
determined. Results from the analysis of the "'H values indicated
about the same degree of potential for dynamic seedabi lity as had been
found in earlier studies in North Dakota (Dennis et al.. 1975b). On
some days, the seeding of larger clouds produced larger increases in
cloud height, while on other days, the smaller clouds showed ITIOre
seeding potential. The tlW analysis indicated frequent occurrences
of increases of a few meters per second in updraft speed, with
larger clouds showing the lTOst promise for seeding according to
this criterion.
6.1.1 Analysis 2.!.. growth itl~!.QQ. height 11981 ~
The earlier Dennis et al. (l975b) analysis of dynamic seedability
in North Dakota clouds wast>ased on morning soundings (1500 GMT). It
is possible that capping inversions appear IOOrp. often in the morning
soundings, while the atmosphere is hetter mixed hy midafternoon
(1500 MDT), so that greater potential for dynamic seeding would he
indicated in the 1500 GMT data. Unfortunately, few 1500 GMT soundings
are available from Baker, but a similar analysis was carried out using
the 1981 Baker data for the closest time that had numerous soundings,
which was 1300 MDT (1900 GMT). Note that soundings were taken at
Baker only on days when convective activity was anticipated, which
involved ahout half of the total number of days, so the results are
not characteristic of all summer days in that region.
Frequency distributions of the rodel cloud top growth (i\H) due
to seeding were plotted for initial [f(Idel updraft diameters of 1, 2,
3, 4, 5. and 6 kilometers. Table 11 summarizes the computed hH values
as il function of updraft size. Some growth, indicated by a positive
"'H value, was found for at least one of the updraft sizes on 26 out
of 37 days analyzed. Figure 38 shows the frequency distrihution for
the maximum "'H value found for each day, while Fig. 39 shows a I'.H
distribution for the 5-km diameter updrafts.
Using these 1900 GMT soundings. the potential for dynamic
seeding effects appears to be somewhat greater than for the 1500 MOT
-58-
TABU: 11
Hodel-Corrputed lIH Values I'm); 1300 HOT (1900 GIl)
Bal:.er, Hontana, 19B1 Sounding Data
------------------------------------------------------------------------
Updraft Diameter (km) Maximum Tropopause
Date 1,0 2.0 3.0 4.0 5.0 6.0 -~ He' ht 'm
5/21 0.6 2.0 0.5 0.2 0.5 0.3 2.0 12.4
5/22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10.3
5/23 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11.1
5/25 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \l.S
5/26 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12.2
5/28 0.0 0.0 0.0 0.0 0.3 1.0 1.0 11.8
5/29 1.0 .b..!. 0.0 0.0 0.0 0.0 1.9 12.2
6/1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12.4
6/3 1.8 0.3 0.3 0.3 0.0 D•• 1.8 12.2
6/6 U D•• 0.3 0.3 0.0 0.0 0.5 12.6
6/7 0.0 0.0 0.0 0.0 0.0 0.0 0.0
6/' 1.3 0.5 D•• 0.2 0.1 0.3 1.3 11.4
6/10 o.s 0.3 0.5 0.5 0.3 0.7 OJ 12.4
6/11 OJ 0.5 0.3 0.3 0.5 "IJ.5" 0.7 13.2
6/12 0.0 0.0 D.' D.' 0.3 0.0 D.' 13.3
6/13 0.0 0.0 TI "IJ.5" 0.6 0.6 3.1 10.8
6/16 0.0 D.' u.s- 1.3 I.' Ll I.' 11.5
6/18 0.0 0.3 o.r 0.2 10 0.3 0.3 10.8
6/23 0.5 1J.4 0.7 0.5 0.3 10 0.7 11.6
6/27 D.' 0.5 0.0 0.3 0.0 0.0 0.5 15.1
6/28 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11.3
6/30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13.1
7/1 2.3 0.0 0.0 0.' 0.3 0.0 2.3 15.2
7/6 0:0 0.2 0.' 0.3 0.0 D.' 0.' 15.3
7/7 0.' 0.3 10 D.' 0.' 1J.4 0.' 15.7
7/10 U 0.0 0.3 0.3 0.0 0.0 1.3 15.2
7/11 U 0.5 0.3 0.3 0.0 0.0 0.5 15.2
7/12 0.0 lG 0.8 1.0 0.' 0.6 1.0 15.2
7/13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15.2
7/17 0.5 0.6 0.8 0.7 0.3 0.3 0.8 15.1
7/18 0.2 1.0 T.4 0.2 0.3 0.6 2.' 14.7
7/1' 2.3 0.0 0:0 0.0 0.3 0.0 2.3 11.1
7/20 0,2 I.' 0.' 0.5 10 0.3 I.' 11.7
7/27 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12.0
7/2' ~ 0.5 0.2 0.0 0.0 0.0 0.6 15.1
8/2 0.0 0.' 0.0 0.3 0.3 0.0 0.' 11.8
8/' 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15.1
Mean 0.42 0.35 0.36 0.26 0.19 0.22 0.81
-59-
".~-------------------~
1981 BAKER MT - 1300
!O
,
I1AX.IMUMOfLUH (k.lIl
~~: Frequency distribution of max1mufll rrodel predicted cloud top
growth (.II.H) expected due to seeding using 1300 MDT (1900 GMT) 1981
Baker sounding data as fnput.
1981 BAKER "'T - 1300 MOT
.5 !.5 2.5
IJElTAH!k-.l
f.!.L.~: Frequency distribution of rodel predicted lIH values due to
seeding for 5-km diameter updrafts j 1300 MDT Baker 1981 soundings
used as input.
-60-
soundings. The median maximum AH (Fig. 38) is ahout 0.6 km, and ahout
one-third of the days with soundings have maxilllJm b.H values greater
than 1 km. The figures in Table 11 suggest that most of the potential
for dynamic seeding was associated w1th the smaller clouds (updraft
sizes up to about 4 km). In fact, on about half of the days with any
growth potential at all the greatest b.H values occurred for the
smallest nndel clouds (updraft sizes lor 2 kill). Other studies have
also suggested that such clouds represent the best potential for rain
increase seeding in the High Plains (see Dennis ~.!h., 1975a).
Dennis et al. (1975b) suggested that in the North Dakota Pilot
Project, a "d"ayhad good dynamic seedability when the I1y.H1el-indicated
ll.H (based on the 1500 GMT sounding) was greater than 1.2 km for any
of the updraft sizes. If this criterion is used for the NOCMP data,
11 of the 37 days for 1981 would have had good dynamic seedabtlity
according to the rodel analysis of the 1300 f4JT soundlngs. Sl!Iith
et al. (1982) suggested that a day with some AH values greater than
n-:-skiii" could be classified as a good seeding day. On that bas1s,
20 of the 37 days showed some promise for dynam1c seeding.
The median and lrean clOUd top growth values (Table 12) tend to
favor the smaller clouds as well. The clouds w1th l-km updrafts
showed the greatest mean growth. The hi ghest values of AH occurred
for clouds with updraft s1 zes of 3 km or less, and the most frequent
occurrence of AH values greater than 1 km was for 1 and 2 km updraft
diameters.
TABLE 12
SUrmldry of llH Values; 1981 Baker, Montana
1300 HOT (1900 GMT) Sounding Data
Updraft Days With Days Wlth Positive b1i (km)
Diameter (km) Zero 6H Positive AH Median Mean
1.0 19 18 0.50 0.87
2.0 17 10 0.50 0.64
3.0 17 20 0.30 0.65
4.0 15 22 0.30 0.43
5.0 19 18 0.30 0.40
6.0 21 16 0.30 0.51
-61-
6.1.2 Analysis.Q.f.. increases .i.!!..!!£S!Ei.!.!.~~~
The change in maximum updraft speed associated with seeding (llW)
was found by taking the difference between the mdel calculated values
of the maximum updraft speed in the simulated seeded and natural
clouds. Frequency distributions of llW were plotted and analyzed in a
manner similar to that used with the llH values. About two-thi rds of
the days showed some increase in maximum updraft speed for some
updraft sizes (Fig. 40). For those days with a positive ll.W, the
median maximum ll.W was about 3 m S-I. In every case, the maximum I'lW
occurred for the larger clouds (updraft diameters 3 km or greater),
and on half those days with some ll.li values greater than zero, the
maximum occurred for the largest updrafts (6 km diameter). Table 13
shows that the amount of the increase in updraft speed also tended
to increase with the size of the initial updraft.
It is of interest to compare the results for the 5-km updraft
diameters in the NDCMP mdel runs with the corresponding I'lW values
for the same size updrafts obtained for the earlier Rapid Project
(Table 14). As Table 14 shows, il greater percentage of the NDCMP
days had positive llW values; 26 of 37 days (70%) had positive lI.W,
while only 56 of 98 days (57%) had positive I'lW in the Rapid Project.
Part of that difference may be related to differences in the criteria
for deciding when to take soundings. However, the mean and median
llW values for the two projects were rather simi lar.
10
2 3
DELHoNINIlAKlJPORAFT IflI/ser.)
~.iQ.: Frequency distribution of cloud model prp.dicted increil~es
in maximum updraft speed (llWj due to seeding; 1300 MOT Baker 1981
soundings used as input.
-62-
TABLE 13
Mode l-Computed lIW Values (km); 1300 MDT (l900 GMT)
Baker. Montana, 19B1 Sounding Data
------------------------------------------------------------------------
Updraft Diameter '-'-"'-J,.o-~ Maximum
Date 1.0 2.0 3.0 4.0 ~
5/21 0.9 1.8 1.3 2.3 3.1 3.9 3.9
5/22 0.0 0.0 0.0 0.0 0.0 0:-0- 0.0
5/23 0.0 0.0 0,0 0.0 0.0 0.0 0.0
5/25 0.0 0.0 0,0 0,0 0,0 0,0 0,0
5/26 0.0 0.0 0.0 0,0 0.0 0.0 0.0
5/28 0.0 0.0 0,0 0,0 0.0 0.0 0.0
5/29 0.0 0.0 1.6 ~ 2.5 2.2 2.6
6/1 0.0 0,0 0,0 0.0 0.0 0.0 0.0
6/3 0.6 2.3 2,5 2.3 2.3 2.4 2.5
6/6 0,1 0,0 D.3 1.4 2.7 2.6 2.7
6/7 0.0 0,0 0,0 0.0 0:0 0.0 0.0
6/9 0.0 0.4 2.2 3.0 3.3 3.6 3.6
6/10 0.2 1.5 1.7 2.0 2.0 T:9 2.0
6/11 0.2 1,8 2,3 T.9 IT 3.4 3.4
6/12 0.0 0.0 2.3 3.0 3.0 1:4 3.4
6/13 0.0 0.0 3.4 3.9 4.5 U 4.6
6/16 0.0 0.0 0.1 0.0 0.0 1JJf 0.1
6/18 0.4 1.1 TI 1.2 1.2 1.2 1.2
6/23 0.4 0.8 1.4 2.0 2.2 D 1.3
6/27 1,2 0.9 0,8 2,2 2.7 U 2.7
6/28 0.0 0.0 0.0 0.0 0:0 0,0 0,0
6/30 0,0 0,0 0,3 2.1 .?.2 2,2 2.3
7/1 0.0 0,1 2,2 2,9 2.3 2.3 2.9
7/6 0.0 4.5 4.7 3.9 4.0 4.0 4.7
7/7 2.6 2,5 D 3.1 3.3 3,2 3.3
7/10 0.2 1.7 2.7 2.7 2.i 2.7 2.7
7/11 0.0 0.0 0:0 2.0 T.9 T.O 3.0
7/12 0.1 0.0 0.0 0.0 0.7 T.T 1.7
7/13 0.0 0.0 0,0 0,0 0.0 07) 0.0
7/17 0.0 0,0 0,6 1.5 1.9 1.9 1.9
7/18 0.0 0.2 0.5 1.3 IT U 4.4
7/19 0.0 0.1 2.4 2.8 2.5 U 2.8
7/20 0.3 0.7 0,9 T.9 3.3 3,4 3.4
7/27 0.0 0.0 0.0 0.0 0.0 0:0 0.0
7/29 0.0 1.6 2.0 1.1 2.5 1--2. 2.9
8/2 0.0 1.9 2.3 2.8 2.9 2.6 2.9
8/4 0,0 0,0 3,1 ~ IT 3.1 3.9
Mean 0.19 0.65 1.23 1.67 1.89 2.00 2.10
-63-
TABLE 14
Summary of l1W Values; 1981 Baker, Montana
1300 MDT (1900 GMT) Sounding Oata*
Updraft Days With Days With Positive lIW (m/s)
Diameter (km) ~ Positive llW Median Mean
1.0 25 12 0.3 0.60
2.0 20 17 1.B 1.41
3.0 12 25 2.3 1.82
4.0 12 25 2.9 2.47
5.0 11 26 3.1 2.70
6.0 11 26 1.7 2.85
1966-1968 Rapid Project Data
5.0 42 56 2.3 2.36
*Including comparison with Rapid Project results.
The results from this set of model runs (and those of Ewald, 1983)
indicate that the larger clouds show greater potential increases in
updraft speeds due to seeding. Increased turbulence and mixing within
the clouds and between the clouds and their environment provide one
possible explanation for the changes in llW. This behavior suggests a
mechanism for invigorating larger clouds and perhaps increasing their
precipitation, even though increases in cloud top hei ght rray be less
likely.
6.1.3 Model analysis.Q.!. 1982 Dickinson soundings
Soundings were taken at Dickinson during the 1982 field project
on only 18 days. Tables 15 throu9h 17 summarize results of the GPCM
model analysis of those soundings. Note that these tables include
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TABLE 15
Hodel-Co~uted AH Values (km)
1982 Dickinson Soundlngs
-----------------------------------------------~------ ------------------
Updraft Diameter (km) Haxll1l1l'1
Date/Time (GMT) 1.0 2.0 3.0 4.0 5.0 6.0 ~
18 June/1600 0 0 0.1 0.1 0 0.1 0.1
19 June/1500 0.2 0.' 3.5 3.8 4.1 4.1 4.1
23 June/OOOO 0 0 0 0 0 0 0
23 June/1500 0 0.' 0 0 0,4 0 0.'
23 June/1800 0.2 0.2 0.5 0.3 0.3 0.3 0.5
23 June/2IOO 0.7 '.2 0.3 0 0.' 0 4.2
24 June/OOOO 0 0 0.2 0.2 0.3 0.5 0.5
24 June/1500 0 0 0 0 0 0 0
25 June/1500 0.6 0.5 0.8 0.6 0,' 0.5 0.8
26 June/lSOO 0 0 0 0 0 0 0
26 June12100 0 3.9 0 0.3 0 0 3.9
27 June/15DO 0.2 0.3 0.7 2.6 2.9 3.2 3.2
28 June/1600 0 0 0 0 0 0 0
28 June/18DO 0 0 0 0 0 0 0
29 June/l100 2.7 0 0 0 0 0 2.7
30 June/lSDO 0.' 0.2 0.4 0.' 0.2 0.2 0.'
1 July/ISDn 0.' 0.6 0 0 0 0 0.6
2 July/OOOO 0.3 0 0 0 0 0 0.3
2 July/lSOO 0 '.5 0.3 0.3 0 0 '.5
3 July/lSOO 0 4.1 0.3 0 0 0.3 4.1
4 July/lSDO 0 0 0 0 0 0 0
4 July/2200 0 0 0 0 0 0 0
5 July/OOOO 0 0 0 0.' 0.5 0 0.5
5 July/lSDO 0 0 0.3 0.3 0.3 0 0.3
6 July/lSOO 0 0 0 0 0 0 0
7 July/lSDO 0 0 0 0 0 0 0
a July/lSOO 0.2 0 0 0 0 0 0.2
a July/laoa 0.6 0 0 0 0 0 0.6
a July/2100 0 0 0 0 0.' 0 0.'
g July/DODO '.' 0.3 0 0 0 0 4,'
g Julyl16DO 0 0 0 0 0 0 0
Heao 0.35 0.63 0.24 0.30 0.33 0.30 1.19
-1>5-
TABLE 16
Summary of Max1Jwm AH Values
1982 Dickinson Soundings
----_.._-----------------------------------------------------------
Updraft Tropopause
-!!ill.... Time Max AH Diameter TGMTT \kiiiJ \kii) ~,.
18 June 16 0.1 9.9
19 .Alne 15 4.1 11.6
23 June 00 0 11.8
23 June 15 0.' 12.8
23 June 18 0.5 12.9
23 June 21 '.2 12.9
24 June 00 0.5 13.2
24 June 15 0 12.0
25 June 15 0.8 12.4
26 June 15 0 12.8
26 June 21 3.9 13.2
27 June 15 3.2 14.0
28 June 16 0 13.3
28 June 18 0 12.5
29 June 17 2.7 12.9
30 June 15 0.' 14.3
1 July 15 0.6 13.3
2.A1ly 00 0.3 12.7
2 July 15 '.5 15.5
3 July 15 4.1 12.2
4 July 15 0 12.9
4 July 22 0 12.8
5 July 00 0.5 13,2
5 July 15 0.3 13.1
6 July 15 0 11.1
7 July 15 0 11.5
8 July 15 0.2 12.1
8 July 18 0.6 11.7
8 July 21 0.' 11,8
9 July 00 '.' 12.8
9 July 16 0 12,8
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TABLE 17
Increase in MaxhllJm Updraft Speed (AW) Due to Seeding
1982 Dickinson Soundings; 5-km Updraft Oialll!ter
Date/Time 6W Trii/Sl-
18 June/1600 0.0
19 June/1500 0.2
23 June/OOOO 1.3
23 June/1500 1.5
23 June/1800 3.4
23 June/2iOO 2.2
24 June/OOOO 0.0
24 June/1500 0.0
25 June/ISOO 3.2
26 June/lS00 0.0
26 June/2100 3.3
27 June/1500 0.0
28 June/1600 0.0
28 June/1800 0.0
29 June/1700 2.'
30 June/lS00 3.2
1 July/1500 2.2
2 July/OOOO 1.9
2 July/1500 2.6
3 July/1500 2.5
4 July/1500 1.8
4 July/2200 1.6
S July/OOOO 1.6
S Julyl1S00 2.8
6 July/1500 0.0
7 July/1500 0.0
8 July/1500 1.6
8 July/1800 1.6
8 July/2100 1.8
9 July/OOOO 2.2
9 JulY/1600 0.0
Me" 1.45
-67-
results for all of the soundings talten at Dickinson. rather than just
one sounding per day, and that the 1982 sounding times are given in
GMT. The tabulated results are therefore not directly corrparable
with the 1981 results presented earlier.
Table 15 presents the cloud top growth llH due to seeding for
initfal updraft diameters from 1 to 6 km. The fIIilxilllJrn vertical
growth was 4.5 km; as before. the greatest vertical development due
to seeding often occurred either for the smallest or the largest
initial updraft sizes. The frequency of days with maxilllJm llH values
greater than 1 km is about the same as was found in the 1981 data.
but the frequency of lI.H values greater than 0.5 km is noticeably
less. (That may be related, in part, to the different timing of
the soundin9s used.) Because of the small number of days involved,
no frequency distributions were plotted for the 1982 data.
Table 16 sU1TIllarizes the maxil1lJm lI.H values and the tropopause
heights for each 1982 sounding. About 40S of the soundings exhibit
some potential for significant dynamic growth due to seeding (llH at
least 0.5 km) for clouds of at least some initial updraft sizes.
It is of interest to note how the dynamic seeding potential
can evolve with time during a day. For exuple. on 23 June there
was little evidence of dynamic potential in the 1500 or 1800 GMT
soundings. Indications of a substantial potential appeared in the
2100 GlT sounding, but then disappeared by 0000 GMT124 June. Similar
indications appear in the 26 June and 8 July data. Not many sets
of serial soundings are available from the NDCMP data, so this
aspect has not received IIIJch consideration in our work to date.
Table 17 presents simihr results for the increments in maximum
updraft speeds due to seeding as indicated by the GPCH model. Again,
over half the days showed some potential for increases in updraft
speed, with llW values of 2-3 m S-l being COl1'lT(ln. The values are
quite similar to those found with the 1981 Baker soundings. There
is less evidence of a systematic variation within a day than in
the lI.H values.
6.2 Comparison Q!. Model Predicted Cloud ..!Q2. Growth Due !Q. Seeding
with Observations
One~dimens;onal, steady-state numerical cloud ITlJdels like the
Great Plains Cloud Model (GPCM) have been used to assess the potential
for dynamic seeding in many places. Simpson et al. (1967) were able
to demonstrate that, for tropical comulus cloUds-:-their m:>del did a
fairly good job of predicting both the natural cloud top height and
the growth in cloud top height that would occur following seeding. An
1nvesti9ation based on similar ideas was carried out with SO!ll! of the
data from the NDCMP. This investigation is referred to here as the
-6H Stody.-
-68-
6.2.1 Background
To understand the procedure used, one rrust consider the way in
which such models are employed to assess dynamic seedabi lity. Suppose
a cloud having a given updraft size (diameter) is selected for study.
For that cloud, a IllJdel can be run in both non-seeded and seeded ver­sions
to predict the corresponding maximum cloud top heights. The
cloud itself can either be seeded or not seeded, and the resulting
cloud top height observed. (Often the maxilTltm radar echo height will
be used as a surrogate observation of the cloud top heighL) A matrix
of the possible cloud top height values can be established, using the
notat i on ill ust rated in Tabl e 18. Note that whi 1e both non-seeded and
seeded versions of the rrodel cloud are available, only one version
of the actual cloud can exist (either non-seeded or seeded).
The seedability predicted by the rrodel is, in the notation of
Table 18, (Hm~ - Hmn)' The observed response to treatment is defined
as (Has - Hmn) for seeded clouds and (Hon - Hmn) for non -seeded ones.
The appropriate one of the latter two response values can then be
compared to the predicted seedability to establish the validity of
the model in predicting dynamic seeding effects.
In the tropical cumulus investigation of Sillllson et a1. (1967),
the results were as indicated in Fig, 41, For the non:seeded clouds,
the "observed seeding effect" averaged near zero, regardless of the
predicted seedability. The fact that the scatter about zero effect
was small (except for one cloud) suggests that the mdel did a good
job of predicting the vertical development of those clouds. The
further fact that the observed seeding effect was quite close to the
predicted effect, for mst of the seeded clouds, indicates that the
seeding did have dynamic effects of the sort predicted. Moreover,
the model predicted the magnitUde of those effects rather well.
TABLE 18
Matrix of Symbols for Cloud Top Height Values
Non-seeded
Seeded
-69-
SEEDABILITY (PREDICTED)
o 1.0 2.0 3.0 4.0 5.0
5.0 ......-......,.--..,....--..---......,.--.,.
UNSEEDED
0 0 ~ 4.0 a:: D
lU
U)
~ 3.0
I-
~ 2.0
lL..
lL..
lU
1.0
(,!)
~
~O
lU
U)
SEEDED
/
/